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Numéro de publicationUS6266649 B1
Type de publicationOctroi
Numéro de demandeUS 09/157,198
Date de publication24 juil. 2001
Date de dépôt18 sept. 1998
Date de priorité18 sept. 1998
État de paiement des fraisPayé
Autre référence de publicationEP1121658A1, EP1121658A4, WO2000017792A1
Numéro de publication09157198, 157198, US 6266649 B1, US 6266649B1, US-B1-6266649, US6266649 B1, US6266649B1
InventeursGregory D. Linden, Jennifer A. Jacobi, Eric A. Benson
Cessionnaire d'origineAmazon.Com, Inc.
Exporter la citationBiBTeX, EndNote, RefMan
Liens externes: USPTO, Cession USPTO, Espacenet
Collaborative recommendations using item-to-item similarity mappings
US 6266649 B1
Résumé
A recommendations service recommends items to individual users based on a set of items that are known to be of interest to the user, such as a set of items previously purchased by the user. In the disclosed embodiments, the service is used to recommend products to users of a merchant's Web site. The service generates the recommendations using a previously-generated table which maps items to lists of “similar” items. The similarities reflected by the table are based on the collective interests of the community of users. For example, in one embodiment, the similarities are based on correlations between the purchases of items by users (e.g., items A and B are similar because a relatively large portion of the users that purchased item A also bought item B). The table also includes scores which indicate degrees of similarity between individual items. To generate personal recommendations, the service retrieves from the table the similar items lists corresponding to the items known to be of interest to the user. These similar items lists are appropriately combined into a single list, which is then sorted (based on combined similarity scores) and filtered to generate a list of recommended items. Also disclosed are various methods for using the current and/or past contents of a user's electronic shopping cart to generate recommendations. In one embodiment, the user can create multiple shopping carts, and can use the recommendation service to obtain recommendations that are specific to a designated shopping cart. In another embodiment, the recommendations are generated based on the current contents of a user's shopping cart, so that the recommendations tend to correspond to the current shopping task being performed by the user.
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What is claimed is:
1. In a multi-user computer system that provides user access to a database of items, a method of recommending items to a user, the method comprising the computer-implemented steps of:
(a) generating a non-user-specific data structure which maps individual items of the database to corresponding sets of similar items in which similarities between items are based at least upon the collective item interests of a community of users;
(b) identifying items that are known to be of interest to the user;
(c) for each of a plurality of the items identified in step (b), accessing the data structure to identify a corresponding set of similar items;
(d) combining the sets of similar items identified in step (c) to generate a combined set of additionally similar items; and
(e) recommending at least some of the similar items of the combined set generated in step (d) to the user;
wherein step (a) is performed in an off-line mode, and steps (b)-(e) are performed substantially in real time in response to an online action by the user.
2. The method of claim 1, wherein step (a) comprises analyzing purchase histories of users to identify correlations between purchases of items.
3. The method of claim 2, wherein step (a) comprises, for each of a plurality of popular items, identifying other items for which a relatively large portion of users that purchased the popular item also purchased the other item.
4. The method of claim 3, wherein step (a) comprises calculating, for each (popular item, other item) pair, a similarity score which reflects a number of users that purchased both the popular item and the other item relative to a number of users that purchased at least one of the popular item and the other item.
5. The method of claim 1, wherein step (a) comprises calculating, for each of a plurality of item pairs (item_A, item_B), a similarity score which reflects a number of users that indicated an interest in both item_A and item_B relative to a number of users that indicated an interest in at least one of item_A and item_B.
6. The method of claim 5, wherein step (a) comprises storing the similarity scores within the data structure, and step (d) comprises using the similarity scores to combine sets of similar items.
7. The method of claim 1, wherein step (a) is repeated periodically, so that item similarities reflected in the data structure reflect current preferences of the community of users.
8. The method of claim 1, wherein the computer system implements online shopping carts for allowing users to select and hold items for possible purchase, and step (b) comprises selecting items that are currently in the user's shopping cart.
9. The method of claim 1, wherein step (b) comprises identifying a plurality of items that were previously purchased by the user.
10. The method of claim 1, wherein step (b) comprises selecting only items that have been placed within a designated shopping cart of a plurality of shopping carts of the user, the method thereby generating recommendations that are specific to a role of the designated shopping cart.
11. The method of claim 1, wherein step (d) comprises weighting at least some of the similar items sets identified in step (c) based on information about the user's affinity for corresponding items of known interest.
12. The method of claim 1, wherein the computer system implements online shopping carts for allowing users to select and hold items for possible purchase, and keeps track of deletions of items from shopping carts, and wherein step (e) further comprises selecting to recommend to the user an item that was previously removed from the user's shopping cart.
13. The method of claim 1, wherein step (a)-(e) are performed without requiring any users to have rated items.
14. The method of claim 1, further comprising filtering out similar items identified in step (c) to remove items already purchased by the user.
15. The method of claim 1, further comprising filtering out similar items identified in step (c) to remove items that do not fall within an item category designated by the user.
16. In a multi-user computer system that provides access to a database of items, a system for recommending items to users, comprising:
a first process which determines similarities between items by at least analyzing historical data that reflects item interests of a community of users, the first process generating a non-user-specific data structure which maps items to sets of similar items; and
a second process which generates personal recommendations for a user by accessing the data structure to identify similar items sets that correspond to items known to be of interest to the user, and by combining the identified similar items sets to generate a list of recommended items;
wherein the first process generates the data structure in an off-line mode, and the second process generates and displays the personal recommendations substantially in real time in response to online actions of users.
17. The system of claim 16, wherein the first process determines a similarity between a pair of items, item_A and item_B, by at least calculating a similarity score which reflects a number of users that are interested in both item_A and item_B relative to a number of users that are interested in item_A or item_B.
18. The system of claim 17, wherein the first process determines a similarity between a pair of items, item_A and item_B, by at least calculating a score value which reflects a number of users that purchased both item_A and item_B relative to a number of users that purchased item_A or item_B.
19. The system of claim 16, wherein the first process generates and stores within the data structure similarity scores that indicate degrees of similarity between items, and the second process uses the similarity scores to combine sets of similar items.
20. The system of claim 16, wherein the first process is an off-line process which executes separately from the second process, and the second process generates recommendations substantially in real-time in response to requests from users.
21. The system of claim 16, wherein the first process is executed periodically to generate a new data structure, so that item similarities reflected in the data structure reflect current preferences of the community of users.
22. The system of claim 16, wherein the computer system implements online shopping carts for allowing users to select and hold items for possible purchase, and wherein the second process generates recommendations for the user based on items that are currently in the user's shopping cart.
23. The system of claim 16, wherein the second process weights at least some of the identified similar items sets based on information about the user's affinity for corresponding items of known interest.
24. The system of claim 16, wherein the computer system allows a user to create multiple shopping carts within a single account, and the second process generates shopping cart specific recommendations to allow a user with multiple shopping carts to obtain recommendations specific to a role of a particular shopping cart.
25. The system of claim 16, wherein the system generates personal recommendations without requiring users to rate items.
26. The system of claim 16, wherein the second process filters out items already purchased by the user from the similar items sets identified from the data structure.
27. The system of claim 16, wherein the second process filters out items from the similar items sets identified from the data structure based on item categories specified by users.
28. In a system for generating personalized recommendations of items from a database of items that are accessed by a community of users, a method of recommending items to users, the method comprising:
in an off-line mode, for each of a plurality of first items:
(a) for each of a plurality of other items of the database, generating a respective score which indicates a degree of similarity between the first item and the other item such that the score is based on at least (i) a number of users that are interested in both the first item and the other item, and (ii) a number of users that are interested in the other item;
(b) sorting the plurality of other items according to the score values generated in step (a);
(c) truncating a list of items which results from step (b); and
(d) storing the truncated list generated in step (c) (“similar items list”) together with corresponding scores generated in step (a) in a non-user-specific data structure for subsequent look-up;
subsequently, in response to an action performed by a user, performing the following steps substantially in real time:
(e) for each of a plurality of items that are known to be of interest to the user, accessing the data structure to identify a corresponding similar items list;
(f) combining the similar items lists identified in (e) to generate a combined list of similar items, wherein combining the similar items lists comprises combining scores of like items; and
(g) recommending at least some of the items from the combined list generated in (f) to the user.
29. The method of claim 28, wherein step (a) comprises calculating a score which is further based on the number of users that are interested in the first item.
30. The method as in claim 1, wherein the items are products that are available for online purchase.
31. The system as in claim 16, wherein the items are products that are available for online purchase.
32. The system as in claim 28, wherein the items are products that are available for online purchase.
33. A method of generating instant product recommendations for online users, comprising:
in an off-line mode, generating a data structure which maps each of a plurality of products directly to a corresponding set of similar products in which product similarities are indicated by similarity scores stored within the non-user-specific data structure; and
subsequently, in response to an action by an online user, immediately generating and displaying personal product recommendations for the user by at least (a) accessing the data structure to look up a respective set of similar products and associated similarity scores for each of multiple products known to be of interest to the user, (b) combining the sets of similar products identified in (a) into a ranked set in which rankings are based at least in-part on the similarity scores, and (c) selecting at least some of the products in the ranked set to display to the online user.
34. The method as in claim 33, wherein generating the data structure comprises using customer purchase histories to predict similarities between products.
35. The method as in claim 34, wherein generating the data structure further comprises generating, for each of multiple pairs of products, a similarity score which is based at least on a number of users that purchased both products in the pair.
36. The method as in claim 35, wherein generating the data structure further comprises using the purchase histories to determine popularity levels of products, and using said popularity levels to select the plurality of products for which to identify and store corresponding sets of similar products.
37. The method as in claim 33, wherein the action by the online user is a request to view a personal shopping cart, and the method comprises looking up from the data structure respective similar products sets for each of multiple products represented within the shopping cart.
38. The method as in claim 33, wherein the method comprises inhibiting selection in (c) of products already purchased by the user.
39. The method as in claim 33, wherein the data structure is a B-tree.
40. The method as in claim 33, further comprising replicating the data structure across multiple machines to accommodate heavy loads.
41. The method as in claim 1, wherein (d) comprises generating a ranked set of similar items in which a similar item's ranking reflects whether that similar item appears within more than one of the sets identified in step (c).
42. The system of claim 16, wherein the list of recommended items is a ranked list in which an item's ranking reflects whether that item appears within more than one of said similar items sets.
43. A computer-implemented method of recommending products to users, comprising:
generating, for each of a plurality of pairs of products, a respective score indicating a degree to which the products of the pair are deemed related to one another, wherein the score reflects a frequency with which the products of the pair co-occur within purchase histories of users;
storing the scores in a non-user-specific data structure that maps products to sets of related products; and
using the data structure and the scores to provide personalized product recommendations to each of multiple users.
44. The method as in claim 43, wherein using the data structure and scores to provide personalized product recommendations comprises:
identifying multiple products that are of interest to a user;
for each of the multiple products, accessing the data structure to identify a set of related products, to thereby identify multiple sets of related products;
combining the multiple sets of related products to generate a ranked set of related products in which product rankings reflect corresponding scores within the data structure; and
recommending products to the user from the ranked set.
45. The method as in claim 44, wherein combining the multiple sets comprises combining scores of like products, so that a product's ranking reflects whether or not that product appears within more than one of the multiple sets.
46. The method as in claim 44, wherein identifying multiple products that are of interest to the user comprises identifying products that are currently in a shopping cart of the user.
47. The method as in claim 46, wherein recommending products to the user from the ranked set comprises displaying recommended products within a web page that displays current contents of the shopping cart.
48. The method as in claim 43, wherein using the data structure and scores to provide personalized product recommendations comprises generating and displaying personal recommendations substantially in real time in response to online actions of users.
49. The method as in claim 43, wherein the scores are generated and stored within the data structure in an off-line processing mode.
Description
FIELD OF THE INVENTION

The present invention relates to information filtering and recommendation systems. More specifically, the invention relates to methods for predicting the interests of individual users based on the known interests of a community of users.

BACKGROUND OF THE INVENTION

A recommendation service is a computer-implemented service that recommends items from a database of items. The recommendations are customized to particular users based on information known about the users. One common application for recommendation services involves recommending products to online customers. For example, online merchants commonly provide services for recommending products (books, compact discs, videos, etc.) to customers based on profiles that have been developed for such customers. Recommendation services are also common for recommending Web sites, articles, and other types of informational content to users.

One technique commonly used by recommendation services is known as content-based filtering. Pure content-based systems operate by attempting to identify items which, based on an analysis of item content, are similar to items that are known to be of interest to the user. For example, a content-based Web site recommendation service may operate by parsing the user's favorite Web pages to generate a profile of commonly-occurring terms, and then use this profile to search for other Web pages that include some or all of these terms.

Content-based systems have several significant limitations. For example, content-based methods generally do not provide any mechanism for evaluating the quality or popularity of an item. In addition, content-based methods generally require that the items include some form of content that is amenable to feature extraction algorithms; as a result, content-based systems tend to be poorly suited for recommending movies, music titles, authors, restaurants, and other types of items that have little or no useful, parsable content.

Another common recommendation technique is known as collaborative filtering. In a pure collaborative system, items are recommended to users based on the interests of a community of users, without any analysis of item content. Collaborative systems commonly operate by having the users rate individual items from a list of popular items. Through this process, each user builds a personal profile of ratings data. To generate recommendations for a particular user, the user's profile is initially compared to the profiles of other users to identify one or more “similar users.” Items that were rated highly by these similar users (but which have not yet been rated by the user) are then recommended to the user. An important benefit of collaborative filtering is that it overcomes the above-noted deficiencies of content-based filtering.

As with content-based filtering methods, however, existing collaborative filtering techniques have several problems. One problem is that the user is commonly faced with the onerous task of having to rate items in the database to build up a personal ratings profile. This task can be frustrating, particularly if the user is not familiar with many of the items that are presented for rating purposes. Further, because collaborative filtering relies on the existence of other, similar users, collaborative systems tend to be poorly suited for providing recommendations to users that have unusual tastes.

Another problem with collaborative filtering techniques is that an item in the database normally cannot be recommended until the item has been rated. As a result, the operator of a new collaborative recommendation system is commonly faced with a “cold start” problem in which the service cannot be brought online in a useful form until a threshold quantity of ratings data has been collected. In addition, even after the service has been brought online, it may take months or years before a significant quantity of the database items can be recommended.

Another problem with collaborative filtering methods is that the task of comparing user profiles tends to be time consuming —particularly if the number of users is large (e.g., tens or hundreds of thousands). As a result, a tradeoff tends to exist between response time and breadth of analysis. For example, in a recommendation system that generates real-time recommendations in response to requests from users, it may not be feasible to compare the user's ratings profile to those of all other users. A relatively shallow analysis of the available data (leading to poor recommendations) may therefore be performed.

Another problem with both collaborative and content-based systems is that they generally do not reflect the current preferences of the community of users. In the context of a system that recommends products to customers, for example, there is typically no mechanism for favoring items that are currently “hot sellers.” In addition, existing systems do not provide a mechanism for recognizing that the user may be searching for a particular type or category of item.

SUMMARY OF THE DISCLOSURE

The present invention addresses these and other problems by providing a computer-implemented service and associated methods for generating personalized recommendations of items based on the collective interests of a community of users. An important benefit of the service is that the recommendations are generated without the need for the user, or any other users, to rate items. Another important benefit is that the recommended items are identified using a previously-generated table or other mapping structure which maps individual items to lists of “similar” items. The item similarities reflected by the table are based at least upon correlations between the interests of users in particular items.

The types of items that can be recommended by the service include, without limitation, books, compact discs (“CDs”), videos, authors, artists, item categories Web sites, and chat groups. The service may be implemented, for example, as part of a Web site, online services network, e-mail notification service, document filtering system, or other type of computer system that explicitly or implicitly recommends items to users. In a preferred embodiment described herein, the service is used to recommend works such as book titles and music titles to users of an online merchant's Web site.

In accordance with one aspect of the invention, the mappings of items to similar items (“item-to-item mappings”) are generated periodically, such as once per week, by an off-line process which identifies correlations between known interests of users in particular items. For example, in the embodiment described in detail below, the mappings are generating by periodically analyzing user purchase histories to identify correlations between purchases of items. The similarity between two items is preferably measured by determining the number of users that have an interest in both items relative to the number of users that have an interest in either item (e.g., items A and B are highly similar because a relatively large portion of the users that bought one of the items also bought the other item). The item-to-item mappings could also incorporate other types of similarities, including content-based similarities extracted by analyzing item descriptions or content.

To generate a set of recommendations for a given user, the service retrieves from the table the similar items lists corresponding to items already known to be of interest to the user, and then appropriately combines these lists to generate a list of recommended items. For example, if there are three items that are known to be of interest to the user (such as three items the user recently purchased), the service may retrieve the similar items lists for these three items from the table and combine these lists. Because the item-to-item mappings are regenerated periodically based on up-to-date sales data, the recommendations tend to reflect the current buying trends of the community.

In accordance with another aspect of the invention, the similar items lists read from the table may be appropriately weighted (prior to being combined) based on indicia of the user's affinity for, or current interest in, the corresponding items of known interest. For example, the similar items list for a book that was purchased in the last week may be weighted more heavily than the similar items list for a book that was purchased four months ago. Weighting a similar items list heavily has the effect of increasing the likelihood that the items in that list will be included in the recommendations that are ultimately presented to the user.

An important aspect of the service is that the relatively computation-intensive task of correlating item interests is performed off-line, and the results of this task (item-to-item mappings) stored in a mapping structure for subsequent look-up. This enables the personal recommendations to be generated rapidly and efficiently (such as in real-time in response to a request by the user), without sacrificing breadth of analysis.

Another feature of the invention involves using the current and/or recent contents of the user's shopping cart as inputs to the recommendation service (or to another type of recommendation service which generates recommendations given a unary listing of items). For example, if the user currently has three items in his or her shopping cart, these three items can be treated as the items of known interest for purposes of generating recommendations, in which case the recommendations may be generated and displayed automatically when the user views the shopping cart contents. Using the current and/or recent shopping cart contents as inputs tends to produce recommendations that are highly correlated to the current short-term interests of the user—even if these short term interest differ significantly from the user's general preferences. For example, if the user is currently searching for books on a particular topic and has added several such books to the shopping cart, this method will more likely produce other books that involve the same or similar topics.

Another feature of the invention involves allowing the user to create multiple shopping carts under a single account (such as shopping carts for different family members), and generating recommendations that are specific to a particular shopping cart. For example, the user can be prompted to select a particular shopping cart (or set of shopping carts), and the recommendations can then be generated based on the items that were purchased from or otherwise placed into the designated shopping cart(s). This feature of the invention allows users to obtain recommendations that correspond to the role or purpose (e.g., work versus pleasure) of a particular shopping cart.

Two specific implementations of the service are disclosed, both of which generate personal recommendations using the same type of table. In the first implementation, the recommendations are based on the items that have recently been rated or purchased by the user. In the second implementation, the recommendations are based on the current shopping cart contents of the user.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features of the invention will now be described with reference to the drawings summarized below. These drawings and the associated description are provided to illustrate a preferred embodiment of the invention, and not to limit the scope of the invention.

FIG. 1 illustrates a Web site which implements a recommendation service which operates in accordance with the invention, and illustrates the flow of information between components.

FIG. 2 illustrates a sequence of steps that are performed by the recommendation process of FIG. 1 to generate personalized recommendations.

FIG. 3 illustrates a sequence of steps that are performed by the table generation process of FIG. 1 to generate a similar items table, and illustrates temporary data structures generated during the process.

FIG. 4 is a Venn diagram illustrating a hypothetical purchase history profile of three items.

FIG. 5 illustrates one specific implementation of the sequence of steps of FIG. 2.

FIG. 6 illustrates the general form of a Web pages used to present the recommendations of the FIG. 5 process to the user.

FIG. 7 illustrates another specific implementation of the sequence of steps of FIG. 2.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

The various features and methods of the invention will now be described in the context of a recommendation service, including two specific implementations thereof, that is used to recommend book titles, music titles, video titles, and other types of items to individual users of the Amazon.com Web site. As will be recognized to those skilled in the art, the disclosed methods can also be used to recommend other types of items, including non-physical items. By way of example and not limitation, the disclosed methods can also be used to recommend authors, artists, categories or groups of titles, Web sites, chat groups, movies, television shows, downloadable content, restaurants, and other users.

Throughout the description, reference will be made to various implementation-specific details of the recommendation service, the Amazon.com Web site, and other recommendation services of the Web site. These details are provided in order to fully illustrate preferred embodiments of the invention, and not to limit the scope of the invention. The scope of the invention is set forth in the appended claims.

I. Overview of Web Site and Recommendation Services

The Amazon.com Web site includes functionality for allowing users to search, browse, and make purchases from an online catalog of several million book titles, music titles, video titles, and other types of items. Using a shopping cart feature of the site, users can add and remove items to/from a personal shopping cart which is persistent over multiple sessions. (As used herein, a “shopping cart” is a data structure and associated code which keeps track of items that have been selected by a user for possible purchase.) For example, a user can modify the contents of the shopping cart over a period of time, such as one week, and then proceed to a check out area of the site to purchase the shopping cart contents.

The user can also create multiple shopping carts within a single account. For example, a user can set up separate shopping carts for work and home, or can set up separate shopping carts for each member of the user's family. A preferred shopping cart scheme for allowing users to set up and use multiple shopping carts is disclosed in U.S. application Ser. No. 09/104,942, filed Jun. 25, 1998, titled METHOD AND SYSTEM FOR ELECTRONIC COMMERCE USING MULTIPLE ROLES, the disclosure of which is hereby incorporated by reference.

The site also implements a variety of different recommendation services for recommending book titles, music titles, and/or video titles to users. One such service, known as BookMatcher™, allows users to interactively rate individual books on a scale of 1-5 to create personal item ratings profiles, and applies collaborative filtering techniques to these profiles to generate personal recommendations. The BookMatcher service is described in detail in U.S. application Ser. No. 09/040,171 filed Mar. 17, 1998, the disclosure of which is hereby incorporated by reference. The site may also include associated services that allow users to rate other types of items, such as CDs and videos. As described below, the ratings data collected by the BookMatcher service and similar services is optionally incorporated into the recommendation processes of the present invention.

Another type of service is a recommendation service which operates in accordance with the invention. The service (“Recommendation Service”) is preferably used to recommend book titles, music titles and/or videos titles to users, but could also be used in the context of the same Web site to recommend other types of items, including authors, artists, and groups or categories of titles. Briefly, given a unary listing of items that are “known” to be of interest to a user (e.g., a list of items purchased, rated, and/or viewed by the user), the Recommendation Service generates a list of additional items (“recommendations”) that are predicted to be of interest to the user. (As used herein, the term “interest” refers generally to a user's liking of or affinity for an item; the term “known” is used to distinguish items for which the user has implicitly or explicitly indicated some level of interest from items predicted by the Recommendation Service to be of interest.)

The recommendations are generated using a table which maps items to lists of “similar” items (“similar items lists”), without the need for users to rate any items (although ratings data may optionally be used). For example, if there are three items that are known to be of interest to a particular user (such as three items the user recently purchased), the service may retrieve the similar items lists for these three items from the table, and appropriately combine these lists (as described below) to generate the recommendations.

In accordance with one aspect of the invention, the mappings of items to similar items (“item-to-item mappings”) are generated periodically, such as once per week, from data which reflects the collective interests of the community of users. More specifically, the item-to-item mappings are generated by an off-line process which identifies correlations between known interests of users in particular items. For example, in the embodiment described in detail below, the mappings are generating by analyzing user purchase histories to identify correlations between purchases of particular items (e.g., items A and B are similar because a relatively large portion of the users that purchased item A also bought item B). The item-to-item mappings could also reflect other types of similarities, including content-based similarities extracted by analyzing item descriptions or content.

An important aspect of the Recommendation Service is that the relatively computation-intensive task of correlating item interests is performed off-line, and the results of this task (item-to-item mappings) are stored in a mapping structure for subsequent look-up. This enables the personal recommendations to be generated rapidly and efficiently (such as in real-time in response to a request by the user), without sacrificing breadth of analysis.

In accordance with another aspect of the invention, the similar items lists read from the table are appropriately weighted (prior to being combined) based on indicia of the user's affinity for or current interest in the corresponding items of known interest. For example, in one embodiment described below, if the item of known interest was previously rated by the user (such as through use of the BookMatcher service), the rating is used to weight the corresponding similar items list. Similarly, the similar items list for a book that was purchased in the last week may be weighted more heavily than the similar items list for a book that was purchased four months ago.

Another feature of the invention involves using the current and/or recent contents of the user's shopping cart as inputs to the Recommendation Service. For example, if the user currently has three items in his or her shopping cart, these three items can be treated as the items of known interest for purposes of generating recommendations, in which case the recommendations may be generated and displayed automatically when the user views the shopping cart contents. If the user has multiple shopping carts, the recommendations are preferably generated based on the contents of the shopping cart implicitly or explicitly designated by the user, such as the shopping cart currently being viewed. This method of generating recommendations can also be used within other types of recommendation systems, including content-based systems and systems that do not use item-to-item mappings.

Using the current and/or recent shopping cart contents as inputs tends to produce recommendations that are highly correlated to the current short-term interests of the user—even if these short term interests are not reflected by the user's purchase history. For example, if the user is currently searching for a father's day gift and has selected several books for prospective purchase, this method will have a tendency to identify other books that are well suited for the gift recipient.

Another feature of the invention involves generating recommendations that are specific to a particular shopping cart. This allows a user who has created multiple shopping carts to conveniently obtain recommendations that are specific to the role or purpose to the particular cart. For example, a user who has created a personal shopping cart for buying books for her children can designate this shopping cart to obtain recommendations of children's books. In one embodiment of this feature, the recommendations are generated based solely upon the current contents of the shopping cart selected for display. In another embodiment, the user may designate one or more shopping carts to be used to generate the recommendations, and the service then uses the items that were purchased from these shopping carts as the items of known interest.

As will be recognized by those skilled in the art, the above-described techniques for using shopping cart contents to generate recommendations can also be incorporated into other types of recommendation systems, including pure content-based systems.

FIG. 1 illustrates the basic components of the Amazon.com Web site 30, including the components used to implement the Recommendation Service. The arrows in FIG. 1 show the general flow of information that is used by the Recommendation Service. As illustrated by FIG. 1, the Web site 30 includes a Web server application 32 (“Web server”) which processes HTTP (Hypertext Transfer Protocol) requests received over the Internet from user computers 34. The Web server 34 accesses a database 36 of HTML (Hypertext Markup Language) content which includes product information pages and other browsable information about the various products of the catalog. The “items” that are the subject of the Recommendation Service are the titles (regardless of media format such as hardcover or paperback) that are represented within this database 36.

The Web site 30 also includes a “user profiles” database 38 which stores account-specific information about users of the site. Because a group of individuals can share an account, a given “user” from the perspective of the Web site may include multiple actual users. As illustrated by FIG. 1, the data stored for each user may include one or more of the following types of information (among other things) that can be used to generate recommendations in accordance with the invention: (a) the user's purchase history, including dates of purchase, (b) the user's item ratings profile (if any), (c) the current contents of the user's personal shopping cart(s), and (d) a listing of items that were recently (e.g., within the last six months) removed from the shopping cart(s) without being purchased (“recent shopping cart contents”). If a given user has multiple shopping carts, the purchase history for that user may include information about the particular shopping cart used to make each purchase; preserving such information allows the Recommendation Service to be configured to generate recommendations that are specific to a particular shopping cart.

As depicted by FIG. 1, the Web server 32 communicates with various external components 40 of the site. These external components 40 include, for example, a search engine and associated database (not shown) for enabling users to interactively search the catalog for particular items. Also included within the external components 40 are various order processing modules (not shown) for accepting and processing orders, and for updating the purchase histories of the users.

The external components 40 also include a shopping cart process (not shown) which adds and removes items from the users' personal shopping carts based on the actions of the respective users. (The term “process” is used herein to refer generally to one or more code modules that are executed by a computer system to perform a particular task or set of related tasks.) In one embodiment, the shopping cart process periodically “prunes” the personal shopping cart listings of items that are deemed to be dormant, such as items that have not been purchased or viewed by the particular user for a predetermined period of time (e.g. two weeks). The shopping cart process also preferably generates and maintains the user-specific listings of recent shopping cart contents.

The external components 40 also include recommendation service components 44 that are used to implement the site's various recommendation services. Recommendations generated by the recommendation services are returned to the Web server 32, which incorporates the recommendations into personalized Web pages transmitted to users.

The recommendation service components 44 include a BookMatcher application 50 which implements the above-described BookMatcher service. Users of the BookMatcher service are provided the opportunity to rate individual book titles from a list of popular titles. The book titles are rated according to the following scale:

1=Bad!

2=Not for me

3=OK

4=Liked it

5=Loved it!

Users can also rate book titles during ordinary browsing of the site. As depicted in FIG. 1, the BookMatcher application 50 records the ratings within the user's items rating profile. For example, if a user of the BookMatcher service gives the book Into Thin Air a score of “5,” the BookMatcher application 50 would record the item (by ISBN or other identifier) and the score within the user's item ratings profile. The BookMatcher application 50 uses the users' item ratings profiles to generate personal recommendations, which can be requested by the user by selecting an appropriate hyperlink. As described in detail below, the item ratings profiles are also used by an “Instant Recommendations” implementation of the Recommendation Service.

The recommendation services components 44 also include a recommendation process 52, a similar items table 60, and an off-line table generation process 66, which collectively implement the Recommendation Service. As depicted by the arrows in FIG. 1, the recommendation process 52 generates personal recommendations based on information stored within the similar items table 60, and based on the items that are known to be of interest (“items of known interest”) to the particular user.

In the embodiments described in detail below, the items of known interest are identified based on information stored in the user's profile, such as by selecting all items purchased by the user or all items in the user's shopping cart. In other embodiments of the invention, other types of methods or sources of information could be used to identify the items of known interest. For example, in a service used to recommend Web sites, the items (Web sites) known to be of interest to a user could be identified by parsing a Web server access log and/or by extracting URLs from the “favorite places” list of the user's Web browser. In a service used to recommend restaurants, the items (restaurants) of known interest could be identified by parsing the user's credit card records to identify restaurants that were visited more than once.

The various processes 50, 52, 66 of the recommendation services may run, for example, on one or more Unix or NT based workstations or physical servers (not shown) of the Web site 30. The similar items table 60 is preferably stored as a B-tree data structure to permit efficient look-up, and may be replicated across multiple machines (together with the associated code of the recommendation process 52) to accommodate heavy loads.

II. Similar Items Table (FIG. 1)

The general form and content of the similar items table 60 will now be described with reference to FIG. 1. As this table can take on many alternative forms, the details of the table are intended to illustrate, and not limit, the scope of the invention.

As indicated above, the similar items table 60 maps items to lists of similar items based at least upon the collective interests of the community of users. The similar items table 60 is preferably generated periodically (e.g., once per week) by the off-line table generation process 66. The table generation process 66 generates the table 60 from data that reflects the collective interests of the community of users. In the embodiment described in detail herein, the similar items table is generated exclusively from the purchase histories of the community of users (as depicted in FIG. 1). In other embodiments, the table 60 may additionally or alternatively be generated from other indicia of user-item interests, including indica based on users viewing activities, shopping cart activities, and item rating profiles. For example, the table 60 could be built exclusively from the present and/or recent shopping cart contents of users. The similar items table 60 could also reflect non-collaborative type item similarities, including content-based similarities derived by comparing item contents or descriptions.

Each entry in the similar items table 60 is preferably in the form of a mapping of a popular item 62 to a corresponding list 64 of similar items (“similar items lists”). As used herein, a “popular” item is an item which satisfies some pre-specified popularity criteria. For example, in the embodiment described herein, an item is treated as popular of it has been purchased by more than 30 customers during the life of the Web site. Using this criteria produces a set of popular items (and thus a recommendation service) which grows over time. The similar items list 64 for a given popular item 62 may include other popular items.

In other embodiments involving sales of products, the table 60 may include entries for most or all of the products of the online merchant, rather than just the popular items. In the embodiment described herein, several different types of items (books, CDs, videos, etc.) are reflected within the same table 60, although separate tables could alternatively be generated for each type of item.

Each similar items list 64 consists of the N (e.g., 20) items which, based on correlations between purchases of items, are deemed to be the most closely related to the respective popular item 62. Each item in the similar items list 64 is stored together with a commonality index (“CI”) value which indicates the relatedness of that item to the popular item 62, based on sales of the respective items. A relatively high commonality index for a pair of items ITEM A and ITEM B indicates that a relatively large percentage of users who bought ITEM A also bought ITEM B (and vice versa). A relatively low commonality index for ITEM A and ITEM B indicates that a relatively small percentage of the users who bought ITEM A also bought ITEM B (and vice versa). As described below, the similar items lists are generated, for each popular item, by selecting the N other items that have the highest commonality index values. Using this method, ITEM A may be included in ITEM B's similar items list even though ITEM B in not present in ITEM A's similar items list.

In the embodiment depicted by FIG. 1, the items are represented within the similar items table 60 using product IDs, such as ISBNs or other identifiers. Alternatively, the items could be represented within the table by title ID, where each title ID corresponds to a given “work” regardless of its media format. In either case, different items which correspond to the same work, such as the hardcover and paperback versions of a given book or the VCR cassette and DVD versions of a given video, are preferably treated as a unit for purposes of generating recommendations.

Although the recommendable items in the described system are in the form of book titles, music titles and videos titles, it will be appreciated that the underlying methods and data structures can be used to recommend a wide range of other types of items. For example, in the system depicted by FIG. 1, the Recommendation Service could also be used to recommend authors, artists, and categorizations or groups of works.

III. General Process for Generating Recommendations (FIG. 2)

The general sequence of steps that are performed by the recommendation process 52 to generate a set of personal recommendations will now be described with reference to FIG. 2. This process, and the more specific implementations of the process depicted by FIGS. 5 and 7 (described below), are intended to illustrate, and not limit, the scope of the invention.

The FIG. 2 process is preferably invoked in real-time in response to an online action of the user. For example, in an Instant Recommendations implementation (FIGS. 5 and 6) of the service, the recommendations are generated and displayed in real-time (based on the user's purchase history and/or item ratings profile) in response to selection by the user of a corresponding hyperlink, such as a hyperlink which reads “Instant Book Recommendations” or “Instant Music Recommendations.” In a shopping cart based implementation (FIG. 7), the recommendations are generated (based on the user's current and/or recent shopping cart contents) in real-time when the user initiates a display of a shopping cart, and are displayed on the same Web page as the shopping cart contents. The Instant Recommendations and shopping cart based embodiments are described separately below under corresponding headings.

Any of a variety of other methods can be used to initiate the recommendations generation process and to display the recommendations to the user. For example, the recommendations can automatically be generated periodically and sent to the user by e-mail, in which case the e-mail listing may contain hyperlinks to the product information pages of the recommended items. Further, the personal recommendations could be generated in advance of any request or action by the user, and cached by the Web site 30 until requested.

As illustrated by FIG. 2, the first step (step 80) of the recommendations-generation process involves identifying a set of items that are of known interest to the user. The “knowledge” of the user's interest can be based on explicit indications of interest (e.g., the user rated the item highly) or implicit indications of interest (e.g., the user added the item to a shopping cart). Items that are not “popular items” within the similar items table 60 can optionally be ignored during this step.

In the embodiment depicted in FIG. 1, the items of known interest are selected from one or more of the following groups: (a) items in the user's purchase history (optionally limited to those items purchased from a particular shopping cart); (b) items in the user's shopping cart (or a particular shopping cart designated by the user), (c) items rated by the user (optionally with a score that exceeds a certain threshold, such as two), and (d) items in the “recent shopping cart contents” list associated with a given user or shopping cart. In other embodiments, the items of known interest may additionally or alternatively be selected based on the viewing activities of the user. For example, the recommendations process 52 could select items that were viewed by the user for an extended period of time and/or viewed more than once. Further, the user could be prompted to select items of interest from a list of popular items.

For each item of known interest, the service retrieves the corresponding similar items list 64 from the similar items table 60 (step 82), if such a list exists. If no entries exist in the table 60 for any of the items of known interest, the process 52 may be terminated; alternatively, the process could attempt to identify additional items of interest, such as by accessing other sources of interest information.

In step 84, the similar items lists 64 are optionally weighted based on information about the user's affinity for the corresponding items of known interest. For example, a similar items list 64 may be weighted heavily if the user gave the corresponding popular item a rating of “5” on a scale or 1-5, or if the user purchased multiple copies of the item. Weighting a similar items list 64 heavily has the effect of increasing the likelihood that the items in that list we be included in the recommendations that are ultimately presented to the user. In one implementation described below, the user is presumed to have a greater affinity for recently purchased items over earlier purchased items.

The similar items lists 64 are preferably weighted by multiplying the commonality index values of the list by a weighting value. The commonality index values as weighted by any applicable weighting value are referred to herein as “scores.” In other embodiments, the recommendations may be generated without weighting the similar items lists 64.

If multiple similar items lists 64 are retrieved in step 82, the lists are appropriately combined (step 86), such as by merging the lists while summing the scores of like items. The resulting list is then sorted (step 88) in order of highest-to-lowest score. In step 90, the sorted list is filtered to remove unwanted items. The items removed during the filtering process may include, for example, items that have already been purchased or rated by the user, and items that fall outside any product group (such as music or books), product category (such as non-fiction), or content rating (such as PG or adult) designated by the user. The filtering step could alternatively be performed at a different stage of the process, such as during the retrieval of the similar items lists from the table 60. The result of step 90 is a list (“recommendations list”) of other items to be recommended to the user.

In step 92, one or more additional items are optionally added to the recommendations list. In one embodiment, the items added in step 92 are selected from the set of items (if any) in the user's “recent shopping cart contents” list. As an important benefit of this step, the recommendations include one or more items that the user previously considered purchasing but did not purchase. The items added in step 92 may additionally or alternatively be selected using another recommendations method, such as a content-based method.

Finally, in step 94, a list of the top M (e.g., 15) items of the recommendations list are returned to the Web server 32 (FIG. 1). The Web server incorporates this list into one or more Web pages that are returned to the user, with each recommended item being presented as a hypertextual link to the item's product information page. The recommendations may alternatively be conveyed to the user by email, facsimile, or other transmission method. Further, the recommendations could be presented as advertisements for the recommended items.

IV. Generation of Similar Items Table (FIGS. 3 and 4)

The table-generation process 66 is preferably executed periodically (e.g., once a week) to generate a similar items table 60 that reflects the most recent purchase history data. The recommendation process 52 uses the most recently generated version of the table 60 to generate recommendations.

FIG. 3 illustrates the sequence of steps that are performed by the table generation process 66 to build the similar items table 60. The general form of temporary data structures that are generated during the process are shown at the right of the drawing. As will be appreciated by those skilled in the art, any of a variety of alternative methods could be used to generate the table 60.

As depicted by FIG. 3, the process initially retrieves the purchase histories for all customers (step 100). Each purchase history is in the general form of the user ID of a customer together with a list of the product IDs (ISBNs, etc.) of the items (books, CDs, videos, etc.) purchased by that customer. In embodiments which support multiple shopping carts within a given account, each shopping cart could be treated as a separate customer for purposes of generating the table. For example, if a given user (or group of users that share an account) purchased items from two different shopping carts within the same account, these purchases could be treated as the purchases of separate users.

The product IDs may be converted to title IDs during this process, or when the table 60 is later used to generate recommendations, so that different versions of an item (e.g., hardcover and paperback) are represented as a single item. This may be accomplished, for example, by using a separate database which maps product IDs to title IDs. To generate a similar items table that strongly reflects the current tastes of the community, the purchase histories retrieved in step 100 can be limited to a specific time period, such as the last six months.

In steps 102 and 104, the process generates two temporary tables 102A and 104A. The first table 102A maps individual customers to the items they purchased. The second table 104A maps items to the customers that purchased such items. To avoid the effects of “ballot stuffing,” multiple copies of the same item purchased by a single customer are represented with a single table entry. For example, even if a single customer purchased 4000 copies of one book, the customer will be treated as having purchased only a single copy. In addition, items that were sold to an insignificant number (e.g., <15) of customers are preferably omitted or deleted from the tables 102A, 104B.

In step 106, the process identifies the items that constitute “popular” items. This may be accomplished, for example, by selecting from the item-to-customers table 104A those items that were purchased by more than a threshold number (e.g., 30) of customers. In the context of the Amazon.com Web site, to resulting set of popular items may contain hundreds of thousands or millions of items.

In step 108, the process counts, for each (popular_item, other_item) pair, the number of customers that are in common. A pseudocode sequence for performing this step is listed in Table 1. The result of step 108 is a table that indicates, for each (popular_item, other_item) pair, the number of customers the two have in common. For example, in the hypothetical table 108A of FIG. 3, POPULAR_A and ITEM_B have seventy customers in common, indicating that seventy customers bought both items.

TABLE 1
for each popular_item
for each customer in customers of item
for each other_item in items of customer
increment common-customer-count(popular_time, other_item)

In step 110, the process generates the commonality indexes for each (popular_item, other_item) pair in the table 108A. As indicated above, the commonality index (CI) values are measures of the similarity between two items, with larger CI values indicating greater degrees of similarity. The commonality indexes are preferably generated such that, for a given popular_item, the respective commonality indexes of the corresponding other_items take into consideration both (a) the number of customers that are common to both items, and (b) the total number of customers of the other_item. A preferred method for generating the commonality index values is set forth in the equation below.

TABLE 1
for each popular_item
for each customer in customers of item
for each other_item in items of customer
increment common-customer-count(popular_item, other_item)

FIG. 4 illustrates this method in example form. In the FIG. 4 example, item_P (a popular item) has two “other items,” item_X and item_Y. Item_P has been purchased by 300 customers, item_X by 300 customers, and item_Y by 30,000 customers. In addition, item_P and item_X have 20 customers in common, and item_P and item_Y have 25 customers in common. Applying the equation above to the values shown in FIG. 4 produces the following results:

CI(item_P, item_X)=20/sqrt(300×300))=0.0667

CI(item_P, item_Y)=25/sqrt(300×30,000))=0.0083

Thus, even though items P and Y have more customers in common than items P and X, items P and X are treated as being more similar than items P and Y. This result desirably reflects the fact that the percentage of item_X customers that bought item_P (6.7%) is much greater than the percentage of item_Y customers that bought item_P (0.08%).

Because this equation is symmetrical (i.e., CI(item_A, item_B)=CI(item_B, item_A) ), it is not necessary to separately calculate the Cl value for every location in the table 108A. In other embodiments, an asymmetrical method may be used to generate the CI values. For example, the CI value for a (popular_item, other_item) pair could be generated as (customers of popular_item and other_item)/(customers of other_item).

Following step 110 of FIG. 3, each popular item has a respective “other_items” list which includes all of the other_items from the table 108A and their associated CI values. In step 112, each other_items list is sorted from highest-to-lowest commonality index. Using the FIG. 4 values as an example, item_X would be positioned closer to the top of the item_B's list than item_Y, since 0.014907>0.001643.

In step 114, the sorted other_items lists are filtered by deleting all list entries that have fewer than 3 customers in common. For example, in the other_items list for POPULAR_A in table 108A, ITEM_A would be deleted since POPULAR_A and ITEM_A have only two customers in common. Deleting such entries tends to reduce statistically poor correlations between item sales.

In step 116, the sorted other_items lists are truncated to length N to generate the similar items lists, and the similar items lists are stored in a B-tree table structure for efficient look-up

As indicated above, any of a variety of other methods for evaluating similarities between items could be incorporated into the table generation process 66. For example, the table generation process could compare item contents and/or use previously-assigned product categorizations as additional indicators of item similarities. An important benefit of the FIG. 3 method, however, is that the items need not contain any content that is amenable to feature extraction techniques, and need not be pre-assigned to any categories. For example, the method can be used to generate a similar items table given nothing more than the product IDs of a set of products and user purchase histories with respect to these products.

Another important benefit of the Recommendation Service is that the bulk of the processing (the generation of the similar items table 60) is performed by an off-line process. Once this table has been generated, personalized recommendations can be generated rapidly and efficiently, without sacrificing breadth of analysis.

V. Instant Recommendations Service (FIGS. 5 and 6)

A specific implementation of the Recommendation Service, referred to herein as the Instant Recommendations service, will now be described with reference to FIGS. 5 and 6.

As indicated above, the Instant Recommendations service is invoked by the user by selecting a corresponding hyperlink from a Web page. For example, the user may select an “Instant Book Recommendations” or similar hyperlink to obtain a listing of recommended book titles, or may select a “Instant Music Recommendations” or “Instant Video Recommendations” hyperlink to obtain a listing of recommended music or video titles. As described below, the user can also request that the recommendations be limited to a particular item category, such as “non-fiction,” “jazz” or “comedies.” The Instant Recommendations service generates the recommendations based exclusively on the purchase history and any item ratings profile of the particular user. The service becomes available to the user (i.e., the appropriate hyperlink is presented to the user) once the user has purchased and/or rated a threshold number (e.g. three) of popular items within the corresponding product group. If the user has established multiple shopping carts, the user may also be presented the option of designating a particular shopping cart to be used in generating the recommendations.

FIG. 5 illustrates the sequence of steps that are performed by the Instant Recommendations service to generate personal recommendations. Steps 180-194 in FIG. 5 correspond, respectively, to steps 80-94 in FIG. 2. In step 180, the process 52 identifies all popular items that have been purchased by the user (from a particular shopping cart, if designated) or rated by the user, within the last six months. In step 182, the process retrieves the similar items lists 64 for these popular items from the similar items table 60.

In step 184, the process 52 weights each similar items list based on the duration since the associated popular item was purchased by the user (with recently-purchased items weighted more heavily), or if the popular item was not purchased, the rating given to the popular item by the user. The formula used to generate the weight values to apply to each similar items list is listed in C in Table 2. In this formula, “is_purchased” is a boolean variable which indicates whether the popular item was purchased, “rating” is the rating value (1-5), if any, assigned to the popular item by the user, “order_date” is the date/time (measured in seconds since 1970) the popular item was purchased, “now” is the current date/time (measured in seconds since 1970), and “6 months” is six months in seconds.

TABLE 2
1 Weight = ( (is_purchased ? 5:rating) * 2 − 5)*
2 (1 + (max( (is purchased ? order_date:0)-(now-6 months), 0) )
3 / (6 months))

In line 1 of the formula, if the popular item was purchased, the value “5” (the maximum possible rating value) is selected; otherwise, the user's rating of the item is selected. The selected value (which may range from 1-5) is then multiplied by 2, and 5 is subtracted from the result. The value calculated in line 1 thus ranges from a minimum of −3 (if the item was rated a (“1”) to a maximum of 5 (if the item was purchased or was rated a “5”).

The value calculated in line 1 is multiplied by the value calculated in lines 2 and 3, which can range from a minimum of 1 (if the item was either not purchased or was purchased at least six months ago) to a maximum of 2 (if order_date=now). Thus, the weight can range from a minimum of −6 to a maximum of 10. Weights of zero and below indicate that the user rated the item a “2” or below. Weights higher than 5 indicate that the user actually purchased the item (although a weight of 5 or less is possible even if the item was purchased), with higher values indicating more recent purchases.

The similar items lists 64 are weighted in step 184 by multiplying the CI values of the list by the corresponding weight value. For example, if the weight value for a given popular item is ten, and the similar items list 64 for the popular item is

(productid_A, 0.10), (productid_B, 0.09), (productid_C, 0.08),

the weighted similar items list would be:

(productid_A, 1.0), (productid_BB, 0.9), (productid_C, 0.8),

The numerical values in the weighted similar items lists are referred to as “scores.”

In step 186, the weighted similar items lists are merged (if multiple lists exist) to form a single list. During this step, the scores of like items are summed. For example, if a given other_item appears in three different similar items lists 64, the three scores (including any negative scores) are summed to produce a composite score.

In step 188, the resulting list is sorted from highest-to-lowest score. The effect of the sorting operation is to place the most relevant items at the top of the list. In step 190, the list is filtered by deleting any items that (1) have already been purchased or rated by the user, (2) have a negative score, or (3) do not fall within the designated product group (e.g., books) or category (e.g., “science fiction,” or “jazz”).

In step 192 one or more items are optionally selected from the recent shopping cart contents list (if such a list exists) for the user, excluding items that have been rated by the user or which fall outside the designated product group or category. The selected items, if any, are inserted at randomly-selected locations within the top M (e.g., 15) positions in the recommendations list. Finally, in step 194, the top M items from the recommendations list are returned to the Web server 32, which incorporates these recommendations into one or more Web pages.

The general form of such a Web page is shown in FIG. 6, which lists five recommended items. From this page, the user can select a link associated with one of the recommended items to view the product information page for that item. In addition, the user can select a “more recommendations” button 200 to view additional items from the list of M items. Further, the user can select a “refine your recommendations” link to rate or indicate ownership of the recommended items. Indicating ownership of an item causes the item to be added to the user's purchase history listing.

The user can also select a specific category such as “non-fiction” or “romance” from a drop-down menu 202 to request category-specific recommendations. Designating a specific category causes items in all other categories to be filtered out in step 190 (FIG. 5).

VI. Shopping Cart Based Recommendations (FIG. 7)

Another specific implementation of the Recommendation Service, referred to herein as shopping cart recommendations, will now be described with reference to FIG. 7.

The shopping cart recommendations service is preferably invoked automatically when the user displays the contents of a shopping cart that contains more than a threshold number (e.g., 1) of popular items. The service generates the recommendations based exclusively on the current contents of the shopping cart. As a, result, the recommendations tend to be highly correlated to the user's current shopping interests. In other implementations, the recommendations may also be based on other items that are deemed to be of current interest to the user, such as items in the recent shopping cart contents of the user and/or items recently viewed by the user. Further, other indications of the user's current shopping interests could be incorporated into the process. For example, any search terms typed into the site's search engine during the user's browsing session could be captured and used to perform content-based filtering of the recommended items list.

FIG. 7 illustrates the sequence of steps that are performed by the shopping cart recommendations service to generate a set of shopping-cart-based recommendations. In step 282, the similar items list for each popular item in the shopping cart is retrieved from the similar items table 60. The similar items list for one or more additional items that are deemed to be of current interest could also be retrieved during this step, such as the list for an item recently deleted from the shopping cart or recently viewed for an extended period of time.

In step 286, these similar items lists are merged while summing the commonality index (Cl) values of the list items. In step 288, the resulting list is sorted from highest-to-lowest score. In step 290, the list is filtered to remove any items that exist in the shopping cart or have been purchased or rated by the user. Finally, in step 294, the top M (e.g., 5) items of the list are returned as recommendations. The recommendations are preferably presented to the user on the same Web page (not shown) as the shopping cart contents.

If the user has defined multiple shopping carts, the recommendations generated by the FIG. 7 process may be based solely on the contents of the shopping cart currently selected for display. As described above, this allows the user to obtain recommendations that correspond to the role or purpose of a particular shopping cart (e.g., work versus home).

The various uses of shopping cart contents to generate recommendations as described above can be applied to other types of recommendation systems, including content-based systems. For example, the current and/or past contents of a shopping cart can be used to generate recommendations in a system in which mappings of items to lists of similar items are generated from a computer-based comparison of item contents. Methods for performing content-based similarity analyses of items are well known in the art, and are therefore not described herein.

Although this invention has been described in terms of certain preferred embodiments, other embodiments that are apparent to those of ordinary skill in the art are also within the scope of this invention. For example, although the embodiments described herein employ item lists, other programming methods for keeping track of and combining sets of similar items can be used. Accordingly, the scope of the present invention is intended to be defined only by reference to the appended claims.

In the claims which follow, reference characters used to denote process steps are provided for convenience of description only, and not to imply a particular order for performing the steps.

Citations de brevets
Brevet cité Date de dépôt Date de publication Déposant Titre
US4870579 *1 oct. 198726 sept. 1989Neonics, Inc.System and method of predicting subjective reactions
US4992940 *13 mars 198912 févr. 1991H-Renee, IncorporatedSystem and method for automated selection of equipment for purchase through input of user desired specifications
US4996642 *25 sept. 198926 févr. 1991Neonics, Inc.System and method for recommending items
US5235509 *15 nov. 198910 août 1993Management Information Support, Inc.Customer self-ordering system using information displayed on a screen
US5583763 *9 sept. 199310 déc. 1996Mni InteractiveMethod and apparatus for recommending selections based on preferences in a multi-user system
US5704017 *16 févr. 199630 déc. 1997Microsoft CorporationCollaborative filtering utilizing a belief network
US5749081 *6 avr. 19955 mai 1998Firefly Network, Inc.System and method for recommending items to a user
US5774670 *6 oct. 199530 juin 1998Netscape Communications CorporationPersistent client state in a hypertext transfer protocol based client-server system
US5790426 *30 avr. 19974 août 1998Athenium L.L.C.Automated collaborative filtering system
US579421011 déc. 199511 août 1998Cybergold, Inc.Attention brokerage
US5867799 *4 avr. 19962 févr. 1999Lang; Andrew K.In a computer system
US5884282 *9 avr. 199816 mars 1999Robinson; Gary B.Automated collaborative filtering system
US5905973 *29 sept. 199718 mai 1999Hitachi, Ltd.Shopping basket presentation method for an online shopping system
US5909023 *21 févr. 19971 juin 1999Hitachi, Ltd.Online shopping support method and system for sales promotions based on the purchase history of users
US5909492 *18 juin 19971 juin 1999Open Market, IncorporatedNetwork sales system
US5918014 *26 déc. 199629 juin 1999Athenium, L.L.C.Automated collaborative filtering in world wide web advertising
US6006218 *28 févr. 199721 déc. 1999MicrosoftMethods and apparatus for retrieving and/or processing retrieved information as a function of a user's estimated knowledge
US6018738 *22 janv. 199825 janv. 2000Microsft CorporationMethods and apparatus for matching entities and for predicting an attribute of an entity based on an attribute frequency value
EP0265083A1 *22 sept. 198727 avr. 1988Westinghouse Electric CorporationVideo merchandising apparatus and method
EP0751471A1 *18 juin 19962 janv. 1997Massachusetts Institute Of TechnologyMethod and apparatus for item recommendation using automated collaborative filtering
EP0827063A128 août 19964 mars 1998Philips Electronics N.V.Method and system for selecting an information item
GB2336925A * Titre non disponible
Citations hors brevets
Référence
1"Able Solutions Announces Able Commerce 2.6", PR Newswire, Sep. 1998.*
2"Cdnow Rated Top Music Site by eMarketer, the Authority on Business Online", PR Newswire, Sep. 1998.*
3"COSMOCOM", Computer Telephony, p. 124, Jul. 1998.*
4"Fort Point Partners Teams With LikeMinds to Offer Breakthrough Personalization Technology for Increased Sales Online", Business Wire, p.3110064, Dialog File 16, AN 05510541, Mar. 1998.*
5"GroupLens Recommendation Engine to Standardize Internet Personalization For Singapore's Online Technologies Consortium", Business Wire, Dialog File 20, AN 01951318, Jun. 1998.*
6"Home Box Office Selects Like Minds Personalization Software for Second Network Site", PR Newswire, p. 1117SFM023, Dialog File 148, AN 09869396, Nov. 1997.*
7"ICAT Corporation: iCat's Commerce Suite Makes Setting Up Shop on Net Even Easier Than High Street", M2 Presswire, Feb. 1997.*
8"iCat Electronic Commerce Suite Takes "Best of Show' Award at WebInnovation 97", PR Newswire. Jun. 1997.*
9"Internet World-IBM To Expand E-Comm Features", Newsbytes News Network, Dec. 1996.*
10"LinkShare Launches Affiliates Profiling Software; First to Integrate Personalization Software Into Affiliates Program", PR Newswire, LinkShare Corp., Dialog File 813 AN 1232636, Feb. 1998.*
11"Net Perceptions Closes Second Round of Financing: GroupLens secures No. 1 recommendation system spot with strong endorsement by investment community", Business Wire, p.3020013, Dialog File 16, AN 05495619, Mar. 1998.*
12"Net Perceptions Debuts GroupLens Version 3.0 at Internet World Spring; "Industrial Strength Tool Matures Into Essential Website Technology'", Business Wire, p. 3090007, Dialog File 16, AN 05505690, Mar. 1998.*
13"iCat Electronic Commerce Suite Takes ‘Best of Show’ Award at WebInnovation 97", PR Newswire. Jun. 1997.*
14"Internet World—IBM To Expand E-Comm Features", Newsbytes News Network, Dec. 1996.*
15"Net Perceptions Debuts GroupLens Version 3.0 at Internet World Spring; ‘Industrial Strength Tool Matures Into Essential Website Technology’", Business Wire, p. 3090007, Dialog File 16, AN 05505690, Mar. 1998.*
16A System for Sharing Recommendations, Communications of the ACM, Mar. 1997/vol. 40, No. 3, pp. 59-62.
17Applying Collaborative Filtering to Usenet News, Communications of the ACM, Mar. 1997/vol. 40, No. 3, pp. 77-87.
18Borchers, A. et al., "Ganging up on Information Overload", Computer, pp. 106-108, Apr. 1998.*
19Bradley N. Miller, John T. Riedl, Joseph A. Konstan with Department of Computer Science, University of Minnesota, Experiences with GroupLens: Making Usenet Useful Again, 13 pgs (undated).
20Brier, S.E., "Smart Devices Peep Into Your Grocery Cart", New York Times Co., Section G, p. 3, Col. 3, Circuits, Jul. 1998.*
21Christos Faloutsos and Douglas Oard with University of Maryland, A Survey of Information Retrieval and Filtering Methods, 22 pgs. (undated).
22Combining Social Networks and Collaborative Filtering, Communications of the ACM, Mar. 1997/vol. 40, No. 3, pp. 63-65.
23Content-Based, Collaborative Recommendation, Communications of the ACM, Mar. 1997/vol. 40, No. 3, pp. 66-72.
24Dragan et al., "Advice From the Web", PC Magazine, vol. 16, No. 15, p. 133, Sep. 1997.*
25GroupLens: An Open Architecture for Collaborative Filtering of Netnews, 18 pgs.
26Joaquin Delgado, "Content-based Collaborative Information Filtering".*
27Joaquin Delgado, "Intelligent Collaborative Information Retrieval".*
28Konstan, J. et al., "GroupLens: Applying Collaborative Filtering to Usenet News", Communications of the ACM, vol. 30, No. 3, pp. 77-87, Mar. 1997.*
29Marko Balabanovic and Yoav Shoham, "Content-Based, Collaborative Recommendation," Communications of the ACM, v 40n3, pp. 66-72, Mar. 1997.*
30McMains, A., "Weiss, Whitten, Staliano's", ADWEEK Eastern Edition, vol. 39, No. 24, p. 82, Jun. 1998.*
31Miller, B. et al., "Experiences with GroupLens: Making Usenet Useful Again", 1997 Annual Technical Conference, pp. 219-232, 1997.*
32Nash, E.L., "Direct Marketing; Strategy, Planning, Execution", 3rd Ed., McGraw-Hill, Inc., pp. 165 & 365-6, 1994.*
33Net Perceptions, Inc., White Paper, Building Customer Loyalty and High-Yield Relationships Through GroupLens Collaborative Filtering, 9 pgs., Nov. 22, 1996.
34Personalized Navigation for the Web, Communications of the ACM, Mar. 1997/vol. 40, No. 3, pp. 73-76.
35Pointing the Way: Active Collaborative Filtering, CHI '95 Proceedings Papers, 11 pgs.
36Recommender Systems for Evaluating Computer Messages, Communications of the ACM, Mar. 1997/vol. 40, No. 3, pp. 88 and 89.
37Resnick, P. et al., "Recommender Systems", Communications of the ACM, vol. 40, No. 3, pp. 56-58, Mar. 1997.*
38Rucker J. et al., "Siteseer: Personalized Navigation for the Web", Communications of the ACM, vol. 40, No. 3, pp. 73-76, Mar. 1997.*
39Upendra Shardanand and Pattie Maes with MIT Media-Lab, Social Information Filtering: Algorithms for Automating "Word of Mouth", 8 pgs (undated).
Référencé par
Brevet citant Date de dépôt Date de publication Déposant Titre
US6356879 *9 oct. 199812 mars 2002International Business Machines CorporationContent based method for product-peer filtering
US6415368 *22 déc. 19992 juil. 2002Xerox CorporationSystem and method for caching
US6473738 *23 mars 200029 oct. 2002James Gordon GarrettMultiple-person buying information system with application to on-line merchandizing
US6477509 *6 janv. 20005 nov. 2002Efunz.ComInternet marketing method and system
US6490602 *15 janv. 19993 déc. 2002Wish-List.Com, Inc.Method and apparatus for providing enhanced functionality to product webpages
US6525747 *2 août 199925 févr. 2003Amazon.Com, Inc.Method and system for conducting a discussion relating to an item
US6549941 *22 nov. 199915 avr. 2003Alexa InternetSoftware system and methods for resubmitting form data to related web sites
US663145122 avr. 20027 oct. 2003Xerox CorporationSystem and method for caching
US6636836 *3 sept. 199921 oct. 2003Iwingz Co., Ltd.Computer readable medium for recommending items with multiple analyzing components
US670817420 déc. 200016 mars 2004I2 Technologies, Inc.System and method for retrieving information according to improved matching criteria
US6742032 *17 déc. 199925 mai 2004Xerox CorporationMethod for monitoring and encouraging community activity in a networked environment
US6751600 *30 mai 200015 juin 2004Commerce One Operations, Inc.Method for automatic categorization of items
US677898220 févr. 200317 août 2004Maquis Techtrix, LlcOnline content provider system and method
US6782370 *4 sept. 199724 août 2004Cendant Publishing, Inc.System and method for providing recommendation of goods or services based on recorded purchasing history
US6785671 *17 mars 200031 août 2004Amazon.Com, Inc.System and method for locating web-based product offerings
US6831663 *24 mai 200114 déc. 2004Microsoft CorporationSystem and process for automatically explaining probabilistic predictions
US6850899 *31 mars 20001 févr. 2005Intel CorporationBusiness process and apparatus for online purchases using a rule-based transferable shopping basket
US6876977 *27 juil. 20005 avr. 2005The Foxboro CompanyShared shopping basket management system
US6963848 *2 mars 20008 nov. 2005Amazon.Com, Inc.Methods and system of obtaining consumer reviews
US6963850 *19 août 19998 nov. 2005Amazon.Com, Inc.Computer services for assisting users in locating and evaluating items in an electronic catalog based on actions performed by members of specific user communities
US698589630 sept. 200210 janv. 2006Perttunen Cary DBrowsing methods, articles and apparatus
US701074716 févr. 20027 mars 2006Perttunen Cary DMethod and system for full text search of purchasable books that make the full text inaccessible to users
US7016866 *28 nov. 200021 mars 2006Accenture Sdn. Bhd.System and method for assisting the buying and selling of property
US7016892 *17 nov. 200021 mars 2006Cnet Networks, Inc.Apparatus and method for delivering information over a network
US703195119 juil. 200118 avr. 2006Convergys Information Management Group, Inc.Expert system adapted dedicated internet access guidance engine
US703586418 mai 200025 avr. 2006Endeca Technologies, Inc.Hierarchical data-driven navigation system and method for information retrieval
US706248331 oct. 200113 juin 2006Endeca Technologies, Inc.Hierarchical data-driven search and navigation system and method for information retrieval
US70625102 déc. 199913 juin 2006Prime Research Alliance E., Inc.Consumer profiling and advertisement selection system
US706553231 oct. 200220 juin 2006International Business Machines CorporationSystem and method for evaluating information aggregates by visualizing associated categories
US7072858 *15 nov. 20004 juil. 2006Xpensewise.Com, Inc.System and method for dynamic price setting and facilitation of commercial transactions
US708008231 oct. 200218 juil. 2006International Business Machines CorporationSystem and method for finding the acceleration of an information aggregate
US710360931 oct. 20025 sept. 2006International Business Machines CorporationSystem and method for analyzing usage patterns in information aggregates
US7113917 *7 mai 200126 sept. 2006Amazon.Com, Inc.Personalized recommendations of items represented within a database
US7117163 *15 juin 20003 oct. 2006I2 Technologies Us, Inc.Product substitution search method
US7127414 *14 oct. 199924 oct. 2006Awadallah Amr AMethods and computer-readable media for processing web-based new and used good comparison shopping
US7127418 *22 mars 200424 oct. 2006Fuisz Richard CMethods for determining customer motivations in purchasing decisions
US712747330 sept. 200224 oct. 2006Sap AktiengesellschaftMethods and systems for providing supplemental contextual content
US713084431 oct. 200231 oct. 2006International Business Machines CorporationSystem and method for examining, calculating the age of an document collection as a measure of time since creation, visualizing, identifying selectively reference those document collections representing current activity
US7130860 *27 sept. 200131 oct. 2006Sony France S.A.Method and system for generating sequencing information representing a sequence of items selected in a database
US715897110 avr. 20022 janv. 2007Thomas Layne BascomMethod for searching document objects on a network
US715901116 août 20042 janv. 2007Maquis Techtrix, LlcSystem and method for managing an online message board
US715902316 déc. 20032 janv. 2007Alexa InternetUse of web usage trail data to identify relationships between browsable items
US716247116 août 20049 janv. 2007Maquis Techtrix LlcContent query system and method
US7174377 *16 janv. 20026 févr. 2007Xerox CorporationMethod and apparatus for collaborative document versioning of networked documents
US7194477 *28 juin 200220 mars 2007Revenue Science, Inc.Optimized a priori techniques
US720080130 août 20023 avr. 2007Sap AktiengesellschaftRich media information portals
US7222085 *22 juin 200422 mai 2007Travelport Operations, Inc.System and method for providing recommendation of goods and services based on recorded purchasing history
US7240353 *24 nov. 20043 juil. 2007General Electric CompanyFunctionality recommendation system
US72461062 juil. 200317 juil. 2007Red Paper LlcSystem and method for distributing electronic information
US7246110 *25 mai 200017 juil. 2007Cnet Networks, Inc.Product feature and relation comparison system
US7249058 *10 avr. 200224 juil. 2007International Business Machines CorporationMethod of promoting strategic documents by bias ranking of search results
US724912331 oct. 200224 juil. 2007International Business Machines CorporationSystem and method for building social networks based on activity around shared virtual objects
US725756931 oct. 200214 août 2007International Business Machines CorporationSystem and method for determining community overlap
US7257573 *28 juil. 200414 août 2007Matsushita Electric Industrial Co., Ltd.Information display apparatus
US7272573 *13 nov. 200118 sept. 2007International Business Machines CorporationInternet strategic brand weighting factor
US730543622 août 20034 déc. 2007Sap AktiengesellschaftUser collaboration through discussion forums
US7313536 *2 juin 200325 déc. 2007W.W. Grainger Inc.System and method for providing product recommendations
US732188730 sept. 200222 janv. 2008Sap AktiengesellschaftEnriching information streams with contextual content
US7324953 *11 août 200029 janv. 2008Danny MurphyDemographic information database processor
US732520116 oct. 200229 janv. 2008Endeca Technologies, Inc.System and method for manipulating content in a hierarchical data-driven search and navigation system
US7343326 *24 mars 200511 mars 2008W.W. GraingerSystem and method for providing product recommendations
US7346559 *14 févr. 200118 mars 2008International Business Machines CorporationSystem and method for automating association of retail items to support shopping proposals
US734666830 août 200218 mars 2008Sap AktiengesellschaftDynamic presentation of personalized content
US7353187 *28 août 20001 avr. 2008Comverse Ltd.Methods and systems for storing predetermined multimedia information
US7360698 *28 janv. 200522 avr. 2008Wyker Kenneth SMethod for determining relevance to customers of an advertisement for retail grocery items offered by a retailer
US737000923 avr. 20016 mai 2008I2 Technologies Us, Inc.Extreme capacity management in an electronic marketplace environment
US737027630 janv. 20036 mai 2008Sap AktiengesellschaftInterface for collecting user preferences
US7373321 *15 févr. 200213 mai 2008Ecost.Com, Inc.System and method for electronic commerce
US738333418 juil. 20063 juin 2008Omniture, Inc.Comparison of website visitation data sets generated from using different navigation tools
US73892419 avr. 200217 juin 2008Thomas Layne BascomMethod for users of a network to provide other users with access to link relationships between documents
US739525930 juil. 20041 juil. 2008A9.Com, Inc.Search engine system and associated content analysis methods for locating web pages with product offerings
US7412202 *3 avr. 200112 août 2008Koninklijke Philips Electronics N.V.Method and apparatus for generating recommendations based on user preferences and environmental characteristics
US7428500 *30 mars 200023 sept. 2008Amazon. Com, Inc.Automatically identifying similar purchasing opportunities
US742852831 mars 200423 sept. 2008Endeca Technologies, Inc.Integrated application for manipulating content in a hierarchical data-driven search and navigation system
US743056130 mars 200630 sept. 2008A9.Com, Inc.Search engine system for locating web pages with product offerings
US74411953 mars 200421 oct. 2008Omniture, Inc.Associating website clicks with links on a web page
US7447705 *19 nov. 20044 nov. 2008Microsoft CorporationSystem and methods for the automatic transmission of new, high affinity media
US7461058 *24 sept. 19992 déc. 2008Thalveg Data Flow LlcOptimized rule based constraints for collaborative filtering systems
US7483921 *17 mai 200627 janv. 2009Panasonic CorporationInformation retrieval apparatus
US7484172 *11 mars 200227 janv. 2009Walker Digital, LlcSystem and method for providing a customized index with hyper-footnotes
US7505959 *19 nov. 200417 mars 2009Microsoft CorporationSystem and methods for the automatic transmission of new, high affinity media
US752306020 déc. 200021 avr. 2009I2 Technologies Us, Inc.System and method for negotiating according to improved matching criteria
US752969331 juil. 20035 mai 2009International Business Machines CorporationMethod and system for designing a catalog with optimized product placement
US754292418 nov. 20042 juin 2009Intel CorporationBusiness process and apparatus for online purchases using a rule-based transferable shopping basket
US754295131 oct. 20052 juin 2009Amazon Technologies, Inc.Strategies for providing diverse recommendations
US75520686 sept. 200523 juin 2009Amazon Technologies, Inc.Methods and systems of obtaining consumer reviews
US755877310 mai 20077 juil. 2009Convergys Cmg Utah, Inc.Expert supported interactive product selection and recommendation
US756791612 sept. 200028 juil. 2009Capital One Financial CorporationSystem and method for performing Web based in-view monitoring
US756795720 avr. 200628 juil. 2009Endeca Technologies, Inc.Hierarchical data-driven search and navigation system and method for information retrieval
US758415931 oct. 20051 sept. 2009Amazon Technologies, Inc.Strategies for providing novel recommendations
US758443526 janv. 20061 sept. 2009Omniture, Inc.Web usage overlays for third-party web plug-in content
US759056229 juin 200515 sept. 2009Google Inc.Product recommendations based on collaborative filtering of user data
US75996854 déc. 20066 oct. 2009Syncronation, Inc.Apparatus for playing of synchronized video between wireless devices
US760337318 nov. 200413 oct. 2009Omniture, Inc.Assigning value to elements contributing to business success
US7617127 *6 févr. 200410 nov. 2009Netflix, Inc.Approach for estimating user ratings of items
US761718421 sept. 200110 nov. 2009Endeca Technologies, Inc.Scalable hierarchical data-driven navigation system and method for information retrieval
US762404714 mars 200324 nov. 2009Amazon Technologies, Inc.Managing server load by varying responses to requests for dynamically-generated web pages
US76274867 oct. 20021 déc. 2009Cbs Interactive, Inc.System and method for rating plural products
US7630916 *25 juin 20038 déc. 2009Microsoft CorporationSystems and methods for improving collaborative filtering
US763667714 mai 200722 déc. 2009Coremetrics, Inc.Method, medium, and system for determining whether a target item is related to a candidate affinity item
US764437518 sept. 20065 janv. 2010Adobe Systems IncorporatedDynamic path flow reports
US76472476 déc. 200412 janv. 2010International Business Machines CorporationMethod and system to enhance web-based shopping collaborations
US76503048 sept. 200019 janv. 2010Capital One Financial CorporationSolicitation to web marketing loop process
US76572246 mai 20032 févr. 2010Syncronation, Inc.Localized audio networks and associated digital accessories
US766081530 juin 20069 févr. 2010Amazon Technologies, Inc.Method and system for occurrence frequency-based scaling of navigation path weights among online content sources
US768095911 juil. 200616 mars 2010Napo Enterprises, LlcP2P network for providing real time media recommendations
US768507419 nov. 200423 mars 2010Amazon.Com, Inc.Data mining of user activity data to identify related items in an electronic catalog
US76851177 juin 200423 mars 2010Hayley Logistics LlcMethod for implementing search engine
US768519230 juin 200623 mars 2010Amazon Technologies, Inc.Method and system for displaying interest space user communities
US7685200 *1 mars 200723 mars 2010Microsoft CorpRanking and suggesting candidate objects
US76894327 juin 200430 mars 2010Hayley Logistics LlcSystem and method for influencing recommender system & advertising based on programmed policies
US7689452 *17 mai 200430 mars 2010Lam Chuck PSystem and method for utilizing social networks for collaborative filtering
US7698165 *1 févr. 200513 avr. 2010AudienceScience Inc.Accepting bids to advertise to users performing a specific activity
US769827910 nov. 200513 avr. 2010Cbs Interactive, Inc.Product feature and relation comparison system
US769842128 déc. 200613 avr. 2010Xerox CorporationMethod and apparatus for collaborative document versioning of networked documents
US77070748 sept. 200327 avr. 2010Accenture Global Services GmbhOnline marketplace channel access
US77160885 août 200211 mai 2010Amazon.Com, Inc.Method and system for electronic commerce using multiple roles
US7716220 *4 juin 200411 mai 2010Realnetworks, Inc.Content recommendation device with an arrangement engine
US77207237 oct. 200218 mai 2010Amazon Technologies, Inc.User interface and methods for recommending items to users
US7725486 *22 mars 200625 mai 2010Panasonic CorporationInformation retrieval apparatus
US77391489 mars 200415 juin 2010Accenture Global Services GmbhReporting metrics for online marketplace sales channels
US77427404 déc. 200622 juin 2010Syncronation, Inc.Audio player device for synchronous playback of audio signals with a compatible device
US774748121 mai 200729 juin 2010I2 Technologies Us, Inc.Extreme capacity management in an electronic marketplace environment
US776145720 déc. 200520 juil. 2010Adobe Systems IncorporatedCreation of segmentation definitions
US77696405 mars 20043 août 2010Accenture Global Services GmbhStrategies for online marketplace sales channels
US777433529 déc. 200510 août 2010Amazon Technologies, Inc.Method and system for determining interest levels of online content navigation paths
US7775886 *30 juin 200517 août 2010Microsoft CorporationTargeted merchandising on a user console
US7778258 *20 juin 200817 août 2010Aol Inc.Distributing personalized content
US777898024 mai 200617 août 2010International Business Machines CorporationProviding disparate content as a playlist of media files
US777901419 oct. 200717 août 2010A9.Com, Inc.Computer processes for adaptively selecting and/or ranking items for display in particular contexts
US778812325 juin 200131 août 2010Ekhaus Michael AMethod and system for high performance model-based personalization
US7788358 *6 mars 200631 août 2010Aggregate KnowledgeUsing cross-site relationships to generate recommendations
US7792697 *8 août 20067 sept. 2010American Express Travel Related Services Company, Inc.System and method for predicting card member spending using collaborative filtering
US779270322 févr. 20067 sept. 2010Qurio Holdings, Inc.Methods, systems, and computer readable medium for generating wish lists
US7793213 *7 juin 20047 sept. 2010About, Inc.Method and apparatus for delivering customized information according to a user's profile
US779719712 nov. 200414 sept. 2010Amazon Technologies, Inc.Method and system for analyzing the performance of affiliate sites
US779742115 déc. 200614 sept. 2010Amazon Technologies, Inc.Method and system for determining and notifying users of undesirable network content
US7801766 *30 mars 200121 sept. 2010You Technology Brand Services, Inc.Method, system, and computer readable medium for facilitating a transaction between a customer, a merchant and an associate
US780960117 oct. 20015 oct. 2010Johnson & Johnson Consumer CompaniesIntelligent performance-based product recommendation system
US78139671 avr. 200812 oct. 2010Ebay Inc.Method and apparatus for listing goods for sale
US7818209 *31 juil. 200319 oct. 2010Campusi, Inc.Best price search engine including coupons
US7827176 *30 juin 20042 nov. 2010Google Inc.Methods and systems for endorsing local search results
US783143229 sept. 20069 nov. 2010International Business Machines CorporationAudio menus describing media contents of media players
US783147620 oct. 20039 nov. 2010Ebay Inc.Listing recommendation in a network-based commerce system
US783154830 sept. 20029 nov. 2010Amazon Technologies, Inc.Systems and methods that use search queries to identify related lists
US783158229 déc. 20059 nov. 2010Amazon Technologies, Inc.Method and system for associating keywords with online content sources
US78356894 déc. 200616 nov. 2010Syncronation, Inc.Distribution of music between members of a cluster of mobile audio devices and a wide area network
US7836051 *13 oct. 200316 nov. 2010Amazon Technologies, Inc.Predictive analysis of browse activity data of users of a database access system in which items are arranged in a hierarchy
US784098614 déc. 200023 nov. 2010Tivo Inc.Intelligent system and methods of recommending media content items based on user preferences
US7848950 *23 déc. 20057 déc. 2010American Express Travel Related Services Company, Inc.Method and apparatus for collaborative filtering of card member transactions
US785348522 nov. 200614 déc. 2010Nec Laboratories America, Inc.Methods and systems for utilizing content, dynamic patterns, and/or relational information for data analysis
US785359431 oct. 200214 déc. 2010International Business Machines CorporationSystem and method for determining founders of an information aggregate
US785643412 nov. 200721 déc. 2010Endeca Technologies, Inc.System and method for filtering rules for manipulating search results in a hierarchical search and navigation system
US786075725 juin 200828 déc. 2010Accenture Global Services LimitedEnhanced transaction fulfillment
US78607595 août 200928 déc. 2010Google Inc.Product recommendations based on collaborative filtering of user data
US786089529 déc. 200528 déc. 2010Amazon Technologies, Inc.Method and system for determining interest spaces among online content sources
US78651374 déc. 20064 janv. 2011Syncronation, Inc.Music distribution system for mobile audio player devices
US7865396 *30 juin 20064 janv. 2011Amazon Technologies, Inc.Managing storage in a networked environment
US786540710 déc. 20074 janv. 2011International Business Machines CorporationSystem and method for automating association of retail items to support shopping proposals
US78655227 nov. 20074 janv. 2011Napo Enterprises, LlcSystem and method for hyping media recommendations in a media recommendation system
US7873765 *31 mars 200518 janv. 2011Google, Inc.Method and system for detection of peripheral devices and communication of related devices
US788582019 juil. 20018 févr. 2011Convergys Cmg Utah, Inc.Expert system supported interactive product selection and recommendation
US78858497 juin 20048 févr. 2011Hayley Logistics LlcSystem and method for predicting demand for items
US78903637 juin 200415 févr. 2011Hayley Logistics LlcSystem and method of identifying trendsetters
US7908183 *31 mars 200615 mars 2011Amazon.Com, Inc.Recommendation system
US790823329 juin 200715 mars 2011International Business Machines CorporationMethod and apparatus for implementing digital video modeling to generate an expected behavior model
US790823729 juin 200715 mars 2011International Business Machines CorporationMethod and apparatus for identifying unexpected behavior of a customer in a retail environment using detected location data, temperature, humidity, lighting conditions, music, and odors
US791275629 sept. 200622 mars 2011Amazon.Com, Inc.Method and system for electronic commerce using multiple roles
US791282331 oct. 200722 mars 2011Endeca Technologies, Inc.Hierarchical data-driven navigation system and method for information retrieval
US79168774 déc. 200629 mars 2011Syncronation, Inc.Modular interunit transmitter-receiver for a portable audio device
US79170824 déc. 200629 mars 2011Syncronation, Inc.Method and apparatus for creating and managing clusters of mobile audio devices
US7917390 *16 févr. 200129 mars 2011Sony CorporationSystem and method for providing customized advertisements over a network
US7921042 *20 août 20075 avr. 2011Amazon.Com, Inc.Computer processes for identifying related items and generating personalized item recommendations
US792107116 nov. 20075 avr. 2011Amazon Technologies, Inc.Processes for improving the utility of personalized recommendations generated by a recommendation engine
US7925723 *31 mars 200612 avr. 2011Qurio Holdings, Inc.Collaborative configuration of a media environment
US7930197 *28 sept. 200619 avr. 2011Microsoft CorporationPersonal data mining
US793021826 mars 200819 avr. 2011Amazon Technologies, Inc.Personalized promotion of new content
US794139420 déc. 200510 mai 2011Adobe Systems IncorporatedUser interface providing summary information or a status pane in a web analytics tool
US794547520 août 200717 mai 2011Amazon.Com, Inc.Computer processes for identifying related items and generating personalized item recommendations
US7945861 *4 sept. 200717 mai 2011Google Inc.Initiating communications with web page visitors and known contacts
US794968123 juil. 200824 mai 2011International Business Machines CorporationAggregating content of disparate data types from disparate data sources for single point access
US7949722 *29 sept. 199924 mai 2011Actv Inc.Enhanced video programming system and method utilizing user-profile information
US795364122 juin 200731 mai 2011Ebay Inc.Method for listing goods for sale by telephone
US795373926 juil. 201031 mai 2011A9.Com, Inc.Automated discovery of items associated with a context based on monitored actions of users
US796621924 sept. 200421 juin 2011Versata Development Group, Inc.System and method for integrated recommendations
US796622427 avr. 200621 juin 2011Amdocs Software Systems LimitedSystem, method and computer program product for generating a relationship-based recommendation
US796633430 sept. 200221 juin 2011Amazon Technologies, Inc.Information retrieval systems and methods that use user-defined lists to identify related offerings
US79663426 mai 200521 juin 2011Hayley Logistics LlcMethod for monitoring link & content changes in web pages
US796639528 sept. 200521 juin 2011Amazon Technologies, Inc.System and method for indicating interest of online content
US7966632 *12 déc. 200721 juin 2011Google Inc.Visual presentation of video recommendations
US797064715 avr. 200528 juin 2011Capital One Financial CorporationSystem and method for performing web based in-view monitoring
US797065631 août 200628 juin 2011Meyer Cordless LlcMethods for determining customer motivations in purchasing decisions
US797066410 déc. 200428 juin 2011Amazon.Com, Inc.Content personalization based on actions performed during browsing sessions
US797092221 août 200828 juin 2011Napo Enterprises, LlcP2P real time media recommendations
US797932227 déc. 201012 juil. 2011Google Inc.Product recommendations based on collaborative filtering of seller products
US79794457 oct. 201012 juil. 2011Amazon Technologies, Inc.Processes for assessing user affinities for particular item categories of a hierarchical browse structure
US798373515 avr. 200319 juil. 2011General Electric CompanySimulation of nuclear medical imaging
US798395314 sept. 201019 juil. 2011Ebay Inc.Method and apparatus for listing goods for sale
US7984056 *28 déc. 200719 juil. 2011Amazon Technologies, Inc.System for facilitating discovery and management of feeds
US799173220 déc. 20052 août 2011Adobe Systems IncorporatedIncrementally adding segmentation criteria to a data set
US799675413 févr. 20069 août 2011International Business Machines CorporationConsolidated content management
US800100328 sept. 200716 août 2011Amazon Technologies, Inc.Methods and systems for searching for and identifying data repository deficits
US80144079 août 20106 sept. 2011Aol Inc.Distributing personalized content
US801975210 nov. 200513 sept. 2011Endeca Technologies, Inc.System and method for information retrieval from object collections with complex interrelationships
US80236634 déc. 200620 sept. 2011Syncronation, Inc.Music headphones for manual control of ambient sound
US802422220 août 200720 sept. 2011Amazon.Com, Inc.Computer processes for identifying related items and generating personalized item recommendations
US802432310 janv. 200820 sept. 2011AudienceScience Inc.Natural language search for audience
US80282372 déc. 200227 sept. 2011Sap AktiengesellschaftPortal-based desktop
US805099826 avr. 20071 nov. 2011Ebay Inc.Flexible asset and search recommendation engines
US805103313 mai 20071 nov. 2011Expanse Networks, Inc.Predisposition prediction using attribute combinations
US80510409 oct. 20071 nov. 2011Ebay Inc.Electronic publication system
US805609229 sept. 20068 nov. 2011Clearspring Technologies, Inc.Method and apparatus for widget-container hosting and generation
US8059646 *13 déc. 200615 nov. 2011Napo Enterprises, LlcSystem and method for identifying music content in a P2P real time recommendation network
US806046330 mars 200515 nov. 2011Amazon Technologies, Inc.Mining of user event data to identify users with common interests
US80604665 nov. 201015 nov. 2011Amazon Technologies, Inc.Service for accepting and selectively exposing user-created lists of items
US806052521 déc. 200715 nov. 2011Napo Enterprises, LlcMethod and system for generating media recommendations in a distributed environment based on tagging play history information with location information
US8073844 *21 avr. 20086 déc. 2011Microsoft CorporationPre-purchase device interoperability validation
US8079046 *14 déc. 200013 déc. 2011Tivo Inc.Intelligent system and methods of recommending media content items based on user preferences
US80822141 juil. 200820 déc. 2011Cbs Interactive Inc.System and methods for rating plural products
US808227918 avr. 200820 déc. 2011Microsoft CorporationSystem and methods for providing adaptive media property classification
US80906068 août 20063 janv. 2012Napo Enterprises, LlcEmbedded media recommendations
US8095426 *19 févr. 200810 janv. 2012Size Me Up, Inc.System and method for comparative sizing between a well-fitting source item and a target item
US81035407 juin 200424 janv. 2012Hayley Logistics LlcSystem and method for influencing recommender system
US8103555 *1 déc. 200824 janv. 2012Sungkyunkwan University Foundation For Corporate CollaborationUser recommendation method and recorded medium storing program for implementing the method
US810825527 sept. 200731 janv. 2012Amazon Technologies, Inc.Methods and systems for obtaining reviews for items lacking reviews
US8108406 *30 déc. 200831 janv. 2012Expanse Networks, Inc.Pangenetic web user behavior prediction system
US81127205 avr. 20077 févr. 2012Napo Enterprises, LlcSystem and method for automatically and graphically associating programmatically-generated media item recommendations related to a user's socially recommended media items
US811707210 avr. 200214 févr. 2012International Business Machines CorporationPromoting strategic documents by bias ranking of search results on a web browser
US811707512 août 200814 févr. 2012Amazon Technologies, Inc.Automatically identifying similar purchasing opportunities
US811719315 août 200814 févr. 2012Lemi Technology, LlcTunersphere
US8117558 *27 nov. 200614 févr. 2012Designin CorporationConverting web content into two-dimensional CAD drawings and three-dimensional CAD models
US8122370 *27 nov. 200621 févr. 2012Designin CorporationVisual bookmarks for home and landscape design
US813221921 déc. 20006 mars 2012Tivo Inc.Intelligent peer-to-peer system and method for collaborative suggestions and propagation of media
US813572214 juin 201013 mars 2012Adobe Systems IncorporatedCreation of segmentation definitions
US8135725 *11 août 200613 mars 2012Yahoo! Inc.System and method for providing tag-based relevance recommendations of bookmarks in a bookmark and tag database
US81403801 mars 201020 mars 2012Amazon.Com, Inc.Creating an incentive to author useful item reviews
US8140388 *7 juin 200420 mars 2012Hayley Logistics LlcMethod for implementing online advertising
US81403912 août 201120 mars 2012Amazon.Com, Inc.Item recommendation service
US815599230 août 201010 avr. 2012Thalveg Data Flow LlcMethod and system for high performance model-based personalization
US81759893 janv. 20088 mai 2012Choicestream, Inc.Music recommendation system using a personalized choice set
US818068016 avr. 200715 mai 2012Jeffrey LeventhalMethod and system for recommending a product over a computer network
US8180688 *29 sept. 201015 mai 2012Amazon Technologies, Inc.Computer-readable medium, system, and method for item recommendations based on media consumption
US8190442 *10 déc. 200429 mai 2012Pace PlcMethod and apparatus for content recommendation
US819047819 oct. 201029 mai 2012American Express Travel Related Services Company, Inc.Method and apparatus for collaborative filtering of card member transactions
US819549926 sept. 20075 juin 2012International Business Machines CorporationIdentifying customer behavioral types from a continuous video stream for use in optimizing loss leader merchandizing
US819604810 sept. 20085 juin 2012Adobe Systems IncorporatedAssociating website clicks with links on a web page
US820060227 mai 200912 juin 2012Napo Enterprises, LlcSystem and method for creating thematic listening experiences in a networked peer media recommendation environment
US820068330 juin 200912 juin 2012Ebay Inc.Determining relevancy and desirability of terms
US820068730 déc. 200512 juin 2012Ebay Inc.System to generate related search queries
US820482118 avr. 201119 juin 2012Well Auctioned, LlcSimulation auction for public offering
US82093782 oct. 200826 juin 2012Clearspring Technologies, Inc.Methods and apparatus for widget sharing between content aggregation points
US82142642 mai 20063 juil. 2012Cbs Interactive, Inc.System and method for an electronic product advisor
US82194023 janv. 200710 juil. 2012International Business Machines CorporationAsynchronous receipt of information from a user
US82247159 août 201017 juil. 2012Amazon Technologies, Inc.Computer-based analysis of affiliate site performance
US82247737 nov. 201117 juil. 2012Amazon Technologies, Inc.Mining of user event data to identify users with common interests
US82341596 nov. 200831 juil. 2012Segmint Inc.Method and system for targeted content placement
US8234584 *18 févr. 200931 juil. 2012Hitachi, Ltd.Computer system, information collection support device, and method for supporting information collection
US823925613 mars 20097 août 2012Segmint Inc.Method and system for targeted content placement
US82446742 déc. 200914 août 2012Gartner, Inc.Interactive peer directory
US824995522 mars 201021 août 2012John Nicholas GrossMethod of testing item availability and delivery performance of an e-commerce site
US8255263 *23 sept. 200228 août 2012General Motors LlcBayesian product recommendation engine
US8255403 *30 déc. 200828 août 2012Expanse Networks, Inc.Pangenetic web satisfaction prediction system
US8260656 *19 avr. 20024 sept. 2012Amazon.Com, Inc.Mining of user-generated playlists for data regarding relationships between digital works
US826622014 sept. 200511 sept. 2012International Business Machines CorporationEmail management and rendering
US82662746 mars 200811 sept. 2012Clearspring Technologies, Inc.Method and apparatus for data processing
US827110713 janv. 200618 sept. 2012International Business Machines CorporationControlling audio operation for data management and data rendering
US8271338 *18 sept. 200918 sept. 2012Netflix, Inc.Approach for estimating user ratings of items
US8271432 *7 juil. 200918 sept. 2012Oracle Otc Subsidiary LlcCommunity-driven relational filtering of unstructured text
US82756731 oct. 200225 sept. 2012Ebay Inc.Method and system to recommend further items to a user of a network-based transaction facility upon unsuccessful transacting with respect to an item
US82803572 juil. 20102 oct. 2012International Business Machines CorporationInformation sharing after proximity connection has ended
US8280894 *22 janv. 20032 oct. 2012Amazon Technologies, Inc.Method and system for maintaining item authority
US828559529 mars 20069 oct. 2012Napo Enterprises, LlcSystem and method for refining media recommendations
US828560219 nov. 20099 oct. 2012Amazon Technologies, Inc.System for recommending item bundles
US82857761 juin 20079 oct. 2012Napo Enterprises, LlcSystem and method for processing a received media item recommendation message comprising recommender presence information
US828622924 mai 20069 oct. 2012International Business Machines CorporationToken-based content subscription
US8290809 *14 févr. 200016 oct. 2012Ebay Inc.Determining a community rating for a user using feedback ratings of related users in an electronic environment
US82908112 août 201116 oct. 2012Amazon Technologies, Inc.Methods and systems for searching for and identifying data repository deficits
US829081819 nov. 200916 oct. 2012Amazon Technologies, Inc.System for recommending item bundles
US82908286 juin 201116 oct. 2012Google Inc.Item recommendations
US829105124 janv. 201116 oct. 2012Qurio Holdings, Inc.Collaborative configuration of a media environment
US8291100 *16 nov. 200416 oct. 2012Sony CorporationService managing apparatus and method, and service providing system and method
US829808714 oct. 200930 oct. 2012Nintendo Of America Inc.Recommendation engine for electronic game shopping channel
US830151223 oct. 200930 oct. 2012Ebay Inc.Product identification using multiple services
US830162322 mai 200730 oct. 2012Amazon Technologies, Inc.Probabilistic recommendation system
US83020122 déc. 200230 oct. 2012Sap AktiengesellschaftProviding status of portal content
US830687421 avr. 20056 nov. 2012Buy.Com, Inc.Method and apparatus for word of mouth selling via a communications network
US830697525 avr. 20066 nov. 2012Worldwide Creative Techniques, Inc.Expanded interest recommendation engine and variable personalization
US83212901 juin 200927 nov. 2012Intel CorporationBusiness process and apparatus for online purchases using a rule-based transferable shopping basket
US832129116 juin 200927 nov. 2012You Technology Brand Service, Inc.Method, system and computer readable medium for facilitating a transaction between a customer, a merchant and an associate
US832667328 déc. 20064 déc. 2012Sprint Communications Company L.P.Carrier data based product inventory management and marketing
US83266905 avr. 20104 déc. 2012Amazon Technologies, Inc.User interface and methods for recommending items to users
US832726617 mai 20074 déc. 2012Napo Enterprises, LlcGraphical user interface system for allowing management of a media item playlist based on a preference scoring system
US833227718 mars 200911 déc. 2012You Technology Brand Services, Inc.Method, system and computer readable medium for facilitating a transaction between a customer, a merchant and an associate
US833241814 juil. 200811 déc. 2012Eharmony, Inc.Collaborative filtering to match people
US833572520 mai 201118 déc. 2012Meyer Cordless LlcMethods for determining customer motivations in purchasing decisions
US83636362 sept. 201129 janv. 2013Aol Inc.Distributing personalized content
US8364685 *27 déc. 200729 janv. 2013Yahoo! Inc.System and method for annotation and ranking of reviews personalized to prior user experience
US83702032 avr. 20105 févr. 2013Amazon Technologies, Inc.User interface and methods for recommending items to users
US838074524 mars 200919 févr. 2013AudienceScience Inc.Natural language search for audience
US8386488 *27 avr. 200426 févr. 2013International Business Machines CorporationMethod and system for matching appropriate content with users by matching content tags and profiles
US838650930 juin 200626 févr. 2013Amazon Technologies, Inc.Method and system for associating search keywords with interest spaces
US8386519 *30 déc. 200826 févr. 2013Expanse Networks, Inc.Pangenetic web item recommendation system
US8392245 *29 sept. 20005 mars 2013Jda Software Group, Inc.System and method for rendering content according to availability data for one or more items
US8392281 *12 août 20085 mars 2013Amazon Technologies, Inc.System and interface for promoting complementary items
US839683411 oct. 200612 mars 2013International Business Machines CorporationReal time web usage reporter using RAM
US839695120 déc. 200712 mars 2013Napo Enterprises, LlcMethod and system for populating a content repository for an internet radio service based on a recommendation network
US840190722 juil. 200819 mars 2013Steve LitzowSystem and method for dynamic price setting and facilitation of commercial transactions
US84020687 déc. 200019 mars 2013Half.Com, Inc.System and method for collecting, associating, normalizing and presenting product and vendor information on a distributed network
US84071056 juin 201126 mars 2013Amazon.Com, Inc.Discovery of behavior-based item relationships based on browsing session records
US840717818 mars 201126 mars 2013Amazon Technologies, Inc.Increasing the diversity of item recommendations by filtering
US842249026 oct. 201016 avr. 2013Napo Enterprises, LlcSystem and method for identifying music content in a P2P real time recommendation network
US842340817 avr. 200616 avr. 2013Sprint Communications Company L.P.Dynamic advertising content distribution and placement systems and methods
US842731824 nov. 200823 avr. 2013International Business Machines CorporationMethod and system for carbon value tracking and labeling
US8429539 *29 juin 201023 avr. 2013Amazon Technologies, Inc.Managing items in a networked environment
US843362118 juin 201230 avr. 2013Amazon.Com, Inc.Discovery of behavior-based item relationships
US843362228 mai 200430 avr. 2013Media Queue, LlcMethod of controlling electronic commerce queue
US843402431 mars 201130 avr. 2013Napo Enterprises, LlcSystem and method for automatically and graphically associating programmatically-generated media item recommendations related to a user's socially recommended media items
US843805220 avr. 20097 mai 2013Amazon Technologies, Inc.Automated selection of three of more items to recommend as a bundle
US844285821 juil. 200614 mai 2013Sprint Communications Company L.P.Subscriber data insertion into advertisement requests
US844338915 nov. 201014 mai 2013Tivo Inc.Intelligent system and methods of recommending media content items based on user preferences
US84527271 août 201128 mai 2013Amazon Technologies, Inc.Service for accepting and selectively exposing user-generated lists
US8452797 *9 mars 201128 mai 2013Amazon Technologies, Inc.Personalized recommendations based on item usage
US845812113 oct. 20114 juin 2013Expanse Networks, Inc.Predisposition prediction using attribute combinations
US84680462 août 201218 juin 2013Amazon.Com, Inc.Playlist-based detection of similar digital works and work creators
US846815519 juin 200718 juin 2013Infosys LimitedCollaborative filtering-based recommendations
US84681649 mars 201118 juin 2013Amazon Technologies, Inc.Personalized recommendations based on related users
US8484203 *4 janv. 20129 juil. 2013Google Inc.Cross media type recommendations for media items based on identified entities
US848422715 oct. 20089 juil. 2013Eloy Technology, LlcCaching and synching process for a media sharing system
US848431117 avr. 20089 juil. 2013Eloy Technology, LlcPruning an aggregate media collection
US848958626 oct. 201016 juil. 2013Google Inc.Methods and systems for endorsing local search results
US849138423 sept. 201123 juil. 2013Samsung Electronics Co., Ltd.Multi-user discovery
US84985732 juil. 201030 juil. 2013International Business Machines CorporationDynamic changes to a user profile based on external service integration
US8504440 *29 sept. 20066 août 2013Dietfood Corp.System and method for automated recipe selection and shopping list creation
US8510134 *22 févr. 201113 août 2013Matrix Healthcare Services, Inc.System and method of providing devices for injuries under worker's compensation coverage
US85101787 juin 201213 août 2013Amazon Technologies, Inc.Computer-based analysis of seller performance
US851024730 juin 200913 août 2013Amazon Technologies, Inc.Recommendation of media content items based on geolocation and venue
US8510325 *24 avr. 200813 août 2013Google Inc.Supplementing search results with information of interest
US851599820 nov. 200620 août 2013Bascom Research LLPFramework for managing document objects stored on a network
US853309424 janv. 200110 sept. 2013Ebay Inc.On-line auction sales leads
US853375713 déc. 201110 sept. 2013Tivo Inc.Intelligent system and methods of recommending media content items based on user preferences
US853886923 févr. 201217 sept. 2013American Express Travel Related Services Company, Inc.Systems and methods for identifying financial relationships
US853896914 nov. 200517 sept. 2013Adobe Systems IncorporatedData format for website traffic statistics
US8539019 *14 janv. 200817 sept. 2013Amazon Technologies, Inc.Managing server load by varying responses to page requests
US854357521 mai 201224 sept. 2013Apple Inc.System for browsing through a music catalog using correlation metrics of a knowledge base of mediasets
US854898731 oct. 20081 oct. 2013Thalveg Data Flow LlcSystem and method for efficiently providing a recommendation
US8554723 *12 juil. 20128 oct. 2013Amazon Technologies, Inc.Mining of user event data to identify users with common interest
US8554891 *20 mars 20088 oct. 2013Sony CorporationMethod and apparatus for providing feedback regarding digital content within a social network
US856617813 sept. 201222 oct. 2013Amazon Technologies, Inc.Methods and systems for searching for and identifying data repository deficits
US857193031 oct. 200529 oct. 2013A9.Com, Inc.Strategies for determining the value of advertisements using randomized performance estimates
US857787419 oct. 20125 nov. 2013Lemi Technology, LlcTunersphere
US8577900 *29 juin 20115 nov. 2013International Business Machines CorporationMethod and apparatus for enhancing webpage browsing
US857804127 déc. 20055 nov. 2013Adobe Systems IncorporatedVariable sampling rates for website visitation analysis
US858350717 oct. 201212 nov. 2013Intel CorporationRule-based transferable shopping basket for online purchases
US858379110 févr. 201212 nov. 2013Napo Enterprises, LlcMaintaining a minimum level of real time media recommendations in the absence of online friends
US85893149 mai 201119 nov. 2013Morris Fritz FriedmanSystem for making financial gifts
US858941818 juil. 201119 nov. 2013Amazon Technologies, Inc.System for facilitating discovery and management of feeds
US858944019 avr. 201119 nov. 2013Pennar Software CorporationAuthentication mechanisms to enable sharing personal information via a networked computer system
US859522617 oct. 200726 nov. 2013Yahoo! Inc.Method and system for providing content according to personal preference
US86008264 avr. 20113 déc. 2013Ebay Inc.Method and apparatus for presenting information relating to a good
US860100330 sept. 20083 déc. 2013Apple Inc.System and method for playlist generation based on similarity data
US860665314 sept. 201210 déc. 2013Google Inc.Item recommendations
US860671710 juil. 200610 déc. 2013Media Queue, LlcPlayable media delivery capacity exchange method
US860681128 sept. 201110 déc. 2013Ebay Inc.Electronic publication system
US8612309 *8 avr. 200517 déc. 2013Sony CorporationPreference information collecting system, device, method, and program
US861231110 juil. 200617 déc. 2013Media Queue, LlcHybrid distribution method for playable media
US861545825 févr. 201324 déc. 2013American Express Travel Related Services Company, Inc.Industry size of wallet
US86206998 août 200631 déc. 2013Napo Enterprises, LlcHeavy influencer media recommendations
US8620717 *4 nov. 200431 déc. 2013Auguri CorporationAnalytical tool
US862076714 mars 201331 déc. 2013Amazon.Com, Inc.Recommendations based on items viewed during a current browsing session
US862091921 mai 201231 déc. 2013Apple Inc.Media item clustering based on similarity data
US863092926 oct. 200614 janv. 2014American Express Travel Related Services Company, Inc.Using commercial share of wallet to make lending decisions
US863096028 mai 200414 janv. 2014John Nicholas GrossMethod of testing online recommender system
US863956327 juin 200728 janv. 2014International Business Machines CorporationGenerating customized marketing messages at a customer level using current events data
US8639688 *12 nov. 200928 janv. 2014Palo Alto Research Center IncorporatedMethod and apparatus for performing context-based entity association
US86398267 mai 200828 janv. 2014Fourthwall Media, Inc.Providing personalized resources on-demand over a broadband network to consumer device applications
US865589929 juin 201218 févr. 2014Expanse Bioinformatics, Inc.Attribute method and system
US865590813 oct. 201118 févr. 2014Expanse Bioinformatics, Inc.Predisposition modification
US865591516 janv. 201318 févr. 2014Expanse Bioinformatics, Inc.Pangenetic web item recommendation system
US8660900 *13 juil. 200625 févr. 2014Perogo, Inc.Multi-site message sharing
US866090712 nov. 201225 févr. 2014Meyer Cordless LlcMethods for determining customer motivations in purchasing decisions
US866103420 juin 201225 févr. 2014Gartner, Inc.Bimodal recommendation engine for recommending items and peers
US866684422 juin 20104 mars 2014Johnson & Johnson Consumer CompaniesIntelligent performance-based product recommendation system
US8669457 *22 déc. 200811 mars 2014Amazon Technologies, Inc.Dynamic generation of playlists
US867112022 janv. 200311 mars 2014Amazon Technologies, Inc.Method and system for manually maintaining item authority
US867680230 nov. 200618 mars 2014Oracle Otc Subsidiary LlcMethod and system for information retrieval with clustering
US868277021 mai 201325 mars 2014American Express Travel Related Services Company, Inc.Using commercial share of wallet in private equity investments
US86884622 févr. 20041 avr. 2014Media Queue, LlcMedia auto exchange system and method
US868853610 mai 20111 avr. 2014Versata Development Group, Inc.Method for integrated recommendations
US86943193 nov. 20058 avr. 2014International Business Machines CorporationDynamic prosody adjustment for voice-rendering synthesized data
US869440321 mai 20138 avr. 2014American Express Travel Related Services Company, Inc.Using commercial share of wallet to rate investments
US8700448 *5 déc. 200115 avr. 2014International Business Machines CorporationSystem and method for item recommendations
US870049029 déc. 200615 avr. 2014Amazon Technologies, Inc.Method, medium, and system for selecting item images for attributes from aggregated sources
US87005382 févr. 200415 avr. 2014Media Queue, LlcMedia exchange system and method
US8712218 *17 déc. 200229 avr. 2014At&T Intellectual Property Ii, L.P.System and method for providing program recommendations through multimedia searching based on established viewer preferences
US87128672 févr. 200429 avr. 2014Media Queue, LlcSystem for providing access to playable media
US871286827 août 201029 avr. 2014Ebay Inc.Listing recommendation using generation of a user-specific query in a network-based commerce system
US871925528 sept. 20056 mai 2014Amazon Technologies, Inc.Method and system for determining interest levels of online content based on rates of change of content access
US87214559 oct. 201213 mai 2014Nintendo Of America, Inc.Recommendation engine for electronic game shopping channel[Wii]
US872574024 mars 200813 mai 2014Napo Enterprises, LlcActive playlist having dynamic media item groups
US8732072 *16 mars 201020 mai 2014Jpmorgan Chase Bank, N.A.System and method for establishing or modifying an account with user selectable terms
US873843224 nov. 200827 mai 2014International Business Machines CorporationSystem and method for segmenting items in a shopping cart by carbon footprint
US873854122 juin 200427 mai 2014Media Queue, LlcMethod of processing rental requests and returns
US87386092 mars 200627 mai 2014Adobe Systems IncorporatedCapturing and presenting site visitation path data
US873930110 janv. 201227 mai 2014Pennar Software CorporationOnline personal library
US8751305 *23 mai 201110 juin 2014140 Proof, Inc.Targeting users based on persona data
US87513071 déc. 201110 juin 2014Hayley Logistics LlcMethod for implementing online advertising
US875133116 nov. 201110 juin 2014Cbs Interactive Inc.System and method for rating plural products
US8751333 *14 févr. 201310 juin 2014Amazon Technologies, Inc.System for extrapolating item characteristics
US876221820 déc. 201024 juin 2014Amazon Technologies, Inc.Method, medium, and system for creating a filtered image set for a product
US87628474 déc. 201224 juin 2014Napo Enterprises, LlcGraphical user interface system for allowing management of a media item playlist based on a preference scoring system
US876893711 mars 20131 juil. 2014Ebay Inc.System and method for retrieving and normalizing product information
US877523827 sept. 20078 juil. 2014International Business Machines CorporationGenerating customized disincentive marketing content for a customer based on customer risk assessment
US877527410 août 20048 juil. 2014Fritch Alibates, LlcSystem, method, and computer program product for a unified internet wallet and gift registry
US877591925 avr. 20068 juil. 2014Adobe Systems IncorporatedIndependent actionscript analytics tools and techniques
US878191719 nov. 201215 juil. 2014W.W. Grainger, Inc.System and method for directing a customer to additional purchasing opportunities
US87819334 oct. 201215 juil. 2014American Express Travel Related Services Company, Inc.Determining commercial share of wallet
US878195423 févr. 201215 juil. 2014American Express Travel Related Services Company, Inc.Systems and methods for identifying financial relationships
US878209915 févr. 201315 juil. 2014Mygobs OyGraphical objects bonding society system and method of operation for a game
US878219717 juil. 201215 juil. 2014Google, Inc.Determining a model refresh rate
US87882834 mai 201222 juil. 2014Expanse Bioinformatics, Inc.Modifiable attribute identification
US87882863 janv. 200822 juil. 2014Expanse Bioinformatics, Inc.Side effects prediction using co-associating bioattributes
US878838811 mars 201322 juil. 2014American Express Travel Related Services Company, Inc.Using commercial share of wallet to rate business prospects
US87932361 nov. 201229 juil. 2014Adobe Systems IncorporatedMethod and apparatus using historical influence for success attribution in network site activity
US8799208 *7 mars 20005 août 2014E-Rewards, Inc.Method and system for evaluating, reporting, and improving on-line promotion effectiveness
US879930229 déc. 20055 août 2014Google Inc.Recommended alerts
US88058311 juin 200712 août 2014Napo Enterprises, LlcScoring and replaying media items
US880587214 sept. 201212 août 2014Google Inc.Supplementing search results with information of interest
US881235529 juin 200719 août 2014International Business Machines CorporationGenerating customized marketing messages for a customer using dynamic customer behavior data
US8819039 *25 août 200326 août 2014Ebay Inc.Method and system to generate a listing in a network-based commerce system
US882540730 oct. 20122 sept. 2014International Business Machines CorporationDetermination of a route of a mobile device in a mobile network
US882552010 mai 20112 sept. 2014Segmint Inc.Targeted marketing to on-hold customer
US882563930 juin 20042 sept. 2014Google Inc.Endorsing search results
US88271475 déc. 20129 sept. 2014Best BuzzDual proprietary and universal mobile barcode reader
US883197227 sept. 20079 sept. 2014International Business Machines CorporationGenerating a customer risk assessment using dynamic customer data
US883912011 mars 201116 sept. 2014Google Inc.Initiating communications with web page visitors and known contacts
US88391411 juin 200716 sept. 2014Napo Enterprises, LlcMethod and system for visually indicating a replay status of media items on a media device
US20040143508 *22 janv. 200322 juil. 2004Shawn BohnMethod and system for maintaining item authority
US20050060280 *14 juil. 200417 mars 2005Collegenet, Inc.Method and apparatus for personalizing completion of electronic forms
US20060168507 *26 janv. 200627 juil. 2006Hansen Kim DApparatus, system, and method for digitally presenting the contents of a printed publication
US20060212349 *24 févr. 200621 sept. 2006Shane BradyMethod and system for delivering targeted banner electronic communications
US20080077471 *6 févr. 200727 mars 2008Cnet Networks, Inc.Controllable automated generator of optimized allied product content
US20080270398 *30 avr. 200730 oct. 2008Landau Matthew JProduct affinity engine and method
US20090182642 *8 juil. 200816 juil. 2009Neelakantan SundaresanMethods and systems to recommend an item
US20100010877 *18 sept. 200914 janv. 2010Neil Duncan HuntApproach for estimating user ratings of items
US20100031178 *18 févr. 20094 févr. 2010Hitachi, Ltd.Computer system, information collection support device, and method for supporting information collection
US20100049627 *19 août 200825 févr. 2010Avaya Inc.Audio Communication Web Site Integration
US20100162115 *22 déc. 200824 juin 2010Erich Lawrence RingewaldDynamic generation of playlists
US20100169340 *30 déc. 20081 juil. 2010Expanse Networks, Inc.Pangenetic Web Item Recommendation System
US20100169342 *30 déc. 20081 juil. 2010Expanse Networks, Inc.Pangenetic Web Satisfaction Prediction System
US20100186041 *22 janv. 200922 juil. 2010Google Inc.Recommending Video Programs
US20100269062 *15 avr. 200921 oct. 2010International Business Machines, CorpoationPresenting and zooming a set of objects within a window
US20100332426 *21 oct. 200930 déc. 2010Alcatel LucentMethod of identifying like-minded users accessing the internet
US20110010331 *7 juil. 200913 janv. 2011Art Technology Group, Inc.Community-Driven Relational Filtering of Unstructured Text
US20110066497 *30 août 201017 mars 2011Choicestream, Inc.Personalized advertising and recommendation
US20110113028 *12 nov. 200912 mai 2011Palo Alto Research Center IncorporatedMethod and apparatus for performing context-based entity association
US20110191311 *3 févr. 20104 août 2011Gartner, Inc.Bi-model recommendation engine for recommending items and peers
US20110197159 *21 avr. 201111 août 2011Naren ChagantiOnline personal library
US20110213661 *1 mars 20101 sept. 2011Joseph MilanaComputer-Implemented Method For Enhancing Product Sales
US20110213786 *25 févr. 20111 sept. 2011International Business Machines CorporationGenerating recommended items in unfamiliar domain
US20110276383 *10 mai 201110 nov. 2011Heiser Ii Russel RobertConsumer-specific advertisement presentation and offer library
US20110282821 *13 mai 201117 nov. 20114-Tell, IncFurther Improvements in Recommendation Systems
US20110288924 *25 mars 201124 nov. 2011David Edward ThomasAdaptable retail pricing environment and electronic exchange, delivering customized shopper rewards
US20110288939 *23 mai 201124 nov. 2011Jon ElvekrogTargeting users based on persona data
US20120005192 *29 juin 20115 janv. 2012International Business Machines CorporationMethod and apparatus for enhancing webpage browsing
US20120036084 *13 avr. 20109 févr. 2012Koninklijke Philips Electronics N.V.Method and system for rating items
US20120041820 *12 août 201016 févr. 2012Mark Allen SimonMachine to structure data as composite property
US20120084155 *1 oct. 20105 avr. 2012Yahoo! Inc.Presentation of content based on utility
US20120084669 *30 sept. 20105 avr. 2012International Business Machines CorporationDynamic group generation
US20120173338 *15 sept. 20105 juil. 2012Behavioreal Ltd.Method and apparatus for data traffic analysis and clustering
US20120226533 *3 mars 20116 sept. 2012Kelly ClarkMethod, system and computer program product for remunerating endorsers of a product and democratizing product sales
US20120278047 *30 déc. 20111 nov. 2012Designin CorporationVisual bookmarks for home and landscape design
US20120278317 *12 juil. 20121 nov. 2012Spiegel Joel RMining of user event data to identify users with common interest
US20120303376 *18 mai 201229 nov. 2012JVC Kenwood CorporationInformation selecting apparatus and method, and computer program
US20130041774 *28 mars 201114 févr. 2013Rakuten, Inc.Product recommendation device, product recommendation method, program, and recording medium
US20130054407 *30 août 201128 févr. 2013Google Inc.System and Method for Recommending Items to Users Based on Social Graph Information
US20130110627 *13 sept. 20122 mai 2013Google Inc.Generating and Presenting Advertisements Based on Context Data for Programmable Search Engines
US20130191377 *20 avr. 201225 juil. 2013Oracle International CorporationSet based item recommendation system
US20130198022 *18 oct. 20111 août 2013Alibaba Group Holding LimitedMethod and Apparatus of Determining A Linked List of Candidate Products
US20140157295 *3 déc. 20125 juin 2014At&T Intellectual Property I, L.P.System and Method of Content and Merchandise Recommendation
USRE417544 févr. 200521 sept. 2010Knight Timothy OUser interface for interacting with online message board
USRE4189912 mars 200326 oct. 2010Apple Inc.System for ranking the relevance of information objects accessed by computer users
USRE4383522 févr. 200727 nov. 2012Maquis Techtrix LlcOnline content tabulating system and method
CN100437570C16 avr. 200426 nov. 2008索尼株式会社Program, data processing method and data processing device
CN100437740C4 mars 200426 nov. 2008奥姆尼图雷有限公司Associating website clicks with links on a web page
EP1844386A2 *6 févr. 200617 oct. 2007Musicstrands, Inc.System for browsing through a music catalog using correlation metrics of a knowledge base of mediasets
EP2153312A1 *27 mars 200817 févr. 2010Amazon Technologies, Inc.Service for providing item recommendations
EP2302533A1 *7 oct. 200330 mars 2011Endeca Technologies, Inc.System and method for manipulating content in a hierarchical data-driven search and navigation system
EP2536120A1 *30 mai 201219 déc. 2012Canon Kabushiki KaishaImage-related handling support system, information processing apparatus, and image-related handling support method
WO2000079453A2 *16 juin 200028 déc. 2000I2 Technologies IncProduct substitution search method
WO2001093067A1 *18 mai 20016 déc. 2001Commerce OneMethod for automatic categorization of items
WO2001098998A1 *15 juin 200127 déc. 2001Catalina Marketing IntMethod of and system for distributing and/or modifying electronic coupons over a network
WO2001099001A1 *15 juin 200127 déc. 2001Catalina Marketing IntMethod and system for distributing coupons over a network prior to consummation of a purchase transaction
WO2004036460A2 *7 oct. 200329 avr. 2004Endeca Technologies IncSystem and method for manipulating content in a hierarchical data-driven search and navigation system
WO2004052010A1 *24 nov. 200317 juin 2004Koninkl Philips Electronics NvRecommendation of video content based on the user profile of users with similar viewing habits
WO2004079551A2 *4 mars 200416 sept. 2004Brett M ErrorAssociating website clicks with links on a web page
WO2005076890A2 *4 févr. 200525 août 2005John Robert CiancuttiApproach for estimating user ratings of items
WO2005122020A1 *8 juin 200522 déc. 2005Marketing Ip Pte Ltd UA shopping system and method
WO2007002828A2 *29 juin 20064 janv. 2007Google IncProduct recommendations based on collaborative filtering of user data
WO2011035298A2 *21 sept. 201024 mars 2011The Nielsen Company (Us) LlcMethods and apparatus to perform choice modeling with substitutability data
WO2011156665A2 *10 juin 201115 déc. 2011Mygobs OyGraphical objects bonding society system and method of operation
WO2013153438A1 *13 avr. 201317 oct. 2013Sentience Technology LtdSystem and method for enabling contextual recommendations and collaboration within content
Classifications
Classification aux États-Unis705/7.29, 705/14.51, 705/14.73, 705/26.7, 705/26.8, 705/335
Classification internationaleG06Q30/00
Classification coopérativeG06Q30/0631, G06Q30/0253, G06Q30/0633, G06Q10/08345, G06Q30/0201, G06Q30/0277, G06Q30/02
Classification européenneG06Q30/02, G06Q30/0633, G06Q30/0253, G06Q30/0201, G06Q30/0277, G06Q10/08345, G06Q30/0631
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Effective date: 19981117